Not logged in. Login | Signup

About ImageNet


Citations and publications
Presentation and Slides
  • L. Fei-Fei and O. Russakovsky, Analysis of Large-Scale Visual Recognition, Bay Area Vision Meeting, October, 2013, pptx | pdf
  • L. Fei-Fei, ImageNet: crowdsourcing, benchmarking & other cool things, CMU VASC Seminar, March, 2010, ppt | pdf
Publications
  • Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. arXiv:1409.0575, 2014. paper | bibtex
  • J. Deng, O. Russakovsky, J. Krause, M. Bernstein, A. Berg, L. Fei-Fei. Scalable multi-label annotation. ACM conference on human factors in computing (CHI), 2014. pdf | bibtex | slides
  • O. Russakovsky, J. Deng, Z. Huang, A. Berg and L. Fei-Fei, Detecting avocados to zucchinis: what have we done, and where are we going?, Proceedings of the International Conference of Computer Vision (ICCV). 2013. pdf | supplement | website | BibTex
  • J. Deng, A. Berg, K. Li and L. Fei-Fei, What does classifying more than 10,000 image categories tell us? Proceedings of the 12th European Conference of Computer Vision (ECCV). 2010. pdf | BibTex
  • O. Russakovsky and L. Fei-Fei, Attribute Learning in Large-scale Datasets. Proceedings of the 12th European Conference of Computer Vision (ECCV), 1st International Workshop on Parts and Attributes. 2010. pdf | Bibtex | slides | data
  • J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database. IEEE Computer Vision and Pattern Recognition (CVPR), 2009. pdf | BibTex
  • J. Deng, K. Li, M. Do, H. Su, L. Fei-Fei, Construction and Analysis of a Large Scale Image Ontology. In Vision Sciences Society (VSS), 2009. pdf | BibTex
Research Projects that use ImageNet
(Please write to support@image-net.org if you have suggestions. )
  • CloudCV. Dhruv Batra, Neelima Chavali, Harsh Agrawal, Prakriti Banik (Virginia Tech).
  • Decaf Image Classifier. Yuanqing Jia, Jeff Donahue, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell (UC Berkeley)

  • Engine for Visual Annotation (EVA). Jia Deng (Stanford University), Jonathan Krause (Stanford University), Zhiheng Huang (Stanford University), Alex Berg (Stony Brook University), Li Fei-Fei (Stanford University).
  • ImageNet Auto-annotation. Matthieu Guillaumin (ETH), Daniel Kuettel (ETH), Vittorio Ferrari (University of Edinburgh).

  • VLG Extractor. Alessandro Bergamo, Chen Fang, Lorenzo Torresani (Dartmouth College)

Papers that use or cite ImageNet
(Please write to support@image-net.org if you have suggestions of other papers or would like to have your publications included here. )
2013
  • Girshick, R., Donahue, J., Darrell, T. and Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv:1311.2524. 2013.
    • Appendix C contains an analysis of cross-dataset redundancy between PASCAL VOC and ILSVRC.
2012
  • Litayem, S. and Joly, A. and Boujemaa, N. Hash-Based Support Vector Machines Approximation for Large Scale Prediction. BMVC, 2012
  • Abbott, J.T. and Austerweil, J.L. and Griffiths, T.L. Constructing a hypothesis space from the Web for large-scale Bayesian word learning. Proceedings of the 34th Annual Conference of the Cognitive Science Society, 2012.
  • Abrate, M. and Bacciu, C. Visualizing word senses in WordNet Atlas. Proceedings of the Eight International Conference on Language Resources and Evaluation, 2012.
  • Anjum, N.A. and Harding, J.A. and Young, RI and Case, K. Mediation of foundation ontology based knowledge sources. Computers in Industry, 2012.
  • Audhkhasi, K. and Narayanan, S. A Globally-Variant Locally-Constant Model for Fusion of Labels from Multiple Diverse Experts Without Using Reference Labels. IEEE Transactions Pattern Analysis and Machine Intelligence, 2012.
  • Bahl, P. and Philipose, M. and Zhong, L. VISION: cloud-powered sight for all: showing the cloud what you see. Proceedings of the third ACM workshop on Mobile cloud computing and services, 2012.
  • Bannour, H. and Hudelot, C. Building Semantic Hierarchies Faithful to Image Semantics. Advances in Multimedia Modeling, 2012.
  • Bannour, H. and Hudelot, C. and others Combinaison d'information visuelle, conceptuelle, et contextuelle pour la construction automatique de hiérarchies sémantiques adaptées à l'annotation d'images. Reconnaissance des Formes et Intelligence Artificielle (RFIA), 2012.
  • Bergamo, A. and Torresani, L. Meta-Class Features for Large-Scale Object Categorization on a Budget. Computer Vision and Pattern Recognition (CVPR), 2012.
  • Chen, N. and Zhou, Q.Y. and Prasanna, V. Understanding web images by object relation network. Proceedings of the 21st international conference on World Wide Web, 2012.
  • Coronato, A. and Gallo, L. Towards abnormal behavior detection of cognitive impaired people. Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012 IEEE International Conference, 2012.
  • Darrell, T. and Song, H.O. and Fritz, M. and Althoff, T. Don't Look Back: Post-hoc Category Detection via Sparse Reconstruction. California Univ Berkeley Dept Of Electrical Engineering And Computer Science, Technical Report, 2012.
  • Divvala, S.K. and Efros, A.A. and Hebert, M. How important are Deformable Parts in the Deformable Parts Model?. Arxiv preprint arXiv:1206.3714, 2012.
  • Ferreira, F. and Jardim-Goncalves, R. Framework for Knowledge Management Based in the Two-Stream Hypothesis. Technological Innovation for Value Creation, 2012.
  • Fröhlich, B. and Rodner, E. and Kemmler, M. and Denzler, J. Large-scale Gaussian process classification using random decision forests. Pattern Recognition and Image Analysis, 2012.
  • Gavves, E. and Snoek, C.G.M. and Smeulders, A.W.M. Convex Reduction of High-Dimensional Kernels for Visual Classification. IEEE Conference on Computer Vision and Pattern Recognition, 2012.
  • Gong, B. and Shi, Y. and Sha, F. and Grauman, K. Geodesic Flow Kernel for Unsupervised Domain Adaptation. IEEE Conference on Computer Vision and Pattern Recognition, 2012.
  • Gong, Y. and Lazebnik, S. and Gordo, A. and Perronnin, F. Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-scale Image Retrieval. IEEE Transaction on Pattern Analysis and Machine Intelligence, TPAMI, 2012.
  • Gordoa, A. and Rodrıguez-Serranoa, J.A. and Perronnina, F. and Valvenyb, E. Leveraging Category-Level Labels For Instance-Level Image Retrieval. Document Analysis Systems (DAS), 10th IAPR International Workshop on, 2012.
  • Guillaumin, M. and Ferrari, S.V. Large-scale Knowledge Transfer for Object Localization in ImageNet. IEEE Conference on Computer Vision & Pattern Recognition (CVPR), 2012.
  • Han, X. and Berg, A.C. DCMSVM: Distributed Parallel Training For Single-Machine Multiclass Classifiers. IEEE Conference on Computer Vision & Pattern Recognition (CVPR), 2012.
  • Harchaoui, Z. and Douze, M. and Paulin, M. and Dudik, M. and Malick, J. Large-scale image classification with trace-norm regularization. IEEE Conference on Computer Vision and Pattern Recognition, 2012.
  • Jain, M. and Benmokhtar, R. and Jégou, H. and Gros, P. Hamming embedding similarity-based image classification. Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, 2012.
  • Jiménez Bernal, H. and others Semantic Label Sharing for Semi-Supervised learning with large datasets. Universitat Politècnica de Catalunya, 2012.
  • Jing, Y. and Rowley, H. and Wang, J. and Tsai, D. and Rosenberg, C. and Covell, M. Google image swirl: a large-scale content-based image visualization system. Proceedings of the 21st international conference companion on World Wide Web, 2012.
  • Jing, Y. and Wang, H.R.J. and Covell, D.T.C.R.M. Google Image Swirl: a large-scale content-based image visualization system.. Proceedings of the 21st World Wide Web Conference, 2012.
  • Joshi, A.J. and Porikli, F. and Papanikolopoulos, N. Scalable active learning for multi-class image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012.
  • Kasper, A. and Xue, Z. and Dillmann, R. The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics. The International Journal of Robotics Research, 2012.
  • Kim, E. and Li, H. and Huang, X. A Hierarchical Image Clustering Cosegmentation Framework. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  • Kramer, G. and Hendriksen, D. and Homminga, M. and Bouma, G. Classifying image galleries into a taxonomy using meta-data and Wikipedia. Proceedings of Natural Language in Database and Information Systems (NLDB), 2012.
  • Kuettel, D. and Guillaumin, M. and Ferrari, V. Segmentation Propagation in ImageNet. European Conference on Computer Vision (ECCV), 2012.
  • Kulkarni, C. and Dow, S.P. and Klemmer, S.R. Early and Repeated Exposure to Examples Improves Creative Work. Cognitive science, 2012.
  • Lütz, A. and Rodner, E. and Denzler, J. I Want To Know More--Efficient Multi-Class Incremental Learning Using Gaussian Processes. Pattern Recognition and Image Analysis, 2012.
  • Lai, K. and Bo, L. and Ren, X. and Fox, D. Detection-based Object Labeling in 3D Scenes. Proc. of International Conference on Robotics and Automation (ICRA), 2012
  • Li, F. and Lebanon, G. and Sminchisescu, C. Chebyshev Approximations to the Histogram chi^2 Kernel. Arxiv preprint arXiv:1206.4074, 2012.
  • Li, L. and Jiang, S. and Huang, Q. Learning Hierarchical Semantic Description via Mixed-norm Regularization for Image Understanding. IEEE Transactions on Multimedia, 2012.
  • Li, X. Content-based visual search learned from social media. SIGMultimedia Rec., Volume 4, Number 1, 2012.
  • Li, Y. and Geng, B. and Tao, D. and Zha, Z. and Yang, L. and Xu, C. Difficulty Guided Image Retrieval using Linear Multiple Feature Embedding. IEEE Transactions on Multimedia, 2012.
  • Li, Y. and Geng, B. and Yang, L. and Xu, C. and Bian, W. Query difficulty estimation for image retrieval. Neurocomputing, 2012.
  • Li, Y. and Zhou, C. and Geng, B. and Xu, C. and Liu, H. A comprehensive study on learning to rank for content based image retrieval. Signal Processing, 2012.
  • Lindner, A. and Bonnier, N. and Sabine, S. What is the Color of Chocolate?–Extracting Color Values of Semantic Expressions. Conference on Colour in Graphics, Imaging, and Vision, University of Amsterdam, 2012.
  • Little, J. and Abrams, A. and Pless, R. Tools for Richer Crowd Source Image Annotations. IEEE Workshop on Applications of Computer Vision (WACV), 2012.
  • Liu, X. and Mu, Y. and Lang, B. and Chang, S.F. Compact hashing for mixed image-keyword query over multi-label images. Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, 2012.
  • Lucchi, A. and Weston, J. Joint Image and Word Sense Discrimination For Image Retrieval. ECCV, 2012.
  • Maji, S. Algorithms and Representations for Visual Recognition. Ph.D. Thesis, 2012.
  • Maji, S. and Berg, A.C. and Malik, J. Efficient Classification for Additive Kernel SVMs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012.
  • May, W. and Fidler, S. and Fazly, A. and Dickinson, S. and Stevenson, S. Unsupervised Disambiguation of Image Captions. *SEM: 1st Joint Conference on Lexical and Computational Semantics, 2012.
  • Motwani, T.S. and Mooney, R.J. Improving Video Activity Recognition using Object Recognition and Text Mining. Proceedings of the 20th European Conference on Artificial Intelligence (ECAI), 2012.
  • Paiton, DM and Brumby, SP and Kenyon, GT and Kunde, GJ and Peterson, KD and Ham, MI and Schultz, PF and George, JS. Combining multiple visual processing streams for locating and classifying objects in video. Image Analysis and Interpretation (SSIAI), 2012 IEEE Southwest Symposium, 2012.
  • Parkhi, O.M. and Vedaldi, A. and Jawahar, A.Z.C.V. Cats and Dogs. Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2012.
  • Patterson, G. and Hays, J. SUN Attribute Database: Discovering, Annotating, and Recognizing Scene Attributes. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  • Pease, A. and Benzmüller, C. Sigma: An integrated development environment for formal ontology. AI Communications (Special Issue on Intelligent Engineering Techniques for Knowledge Bases), 2012.
  • Perronnin, F. and Akata, Z. and Harchaoui, Z. and Schmid, C. and others Towards Good Practice in Large-Scale Learning for Image Classification. Computer Vision and Pattern Recognition, 2012.
  • Ramanan, H.P.D. Detecting Activities of Daily Living in First-person Camera Views. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  • Romberg, S. and Lienhart, R. and Hörster, E. Multimodal image retrieval. International Journal of Multimedia Information Retrieval, 2012.
  • Shalit, U. and Weinshall, D. and Chechik, G. Online Learning in the Embedded Manifold of Low-rank Matrices. Journal of Machine Learning Research, 2012.
  • Song, H.O. and Zickler, S. and Althoff, T. and Girshick, R. and Fritz, M. and Geyer, C. and Felzenszwalb, P. and Darrell, T. Sparselet Models for Efficient Multiclass Object Detection. Proceedings of European Conference on Computer Vision (ECCV), 2012.
  • Su, H. and Deng, J. and Fei-Fei, L. Crowdsourcing Annotations for Visual Object Detection. AAAI 2012 Human Computation Workshop, 2012
  • Vedaldi, A. and Zisserman, A. Sparse Kernel Approximations for Efficient Classification and Detection. Computer Vision and Pattern Recognition (CVPR), 2012.
  • Verma, N. and Mahajan, D. and Sellamanickam, S. and Nair, V. Learning Hierarchical Similarity Metrics. Computer Vision and Pattern Recognition (CVPR), 2012.
  • Vreeswijk, D.T.J. and van de Sande, K.E.A. and Snoek, C.G.M. and Smeulders, A.W.M. All Vehicles are Cars: Subclass Preferences in Container Concepts. ACM International Conference on Multimedia Retrieval, 2012.
  • Wang, G. and Hoiem, D. and Forsyth, D. Learning Image Similarity from Flickr Groups Using Fast Kernel Machines. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012.
  • Wang, J. and Wang, J. and Zeng, G. and Tu, Z. and Gan, R. and Li, S. Scalable k-NN graph construction for visual descriptors. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  • Wang, S. and Huang, Q. and Jiang, S. and Tian, Q. S3MKL: Scalable Semi-Supervised Multiple Kernel Learning for Real World Image Applications. IEEE Transactions on Multimedia, 2012.
  • Wang, S.Y. and Liao, W.S. and Hsieh, L.C. and Chen, Y.Y. and Hsu, W.H. Learning by Expansion: Exploiting Social Media for Image Classification with Few Training Examples. Neurocomputing, 2012.
  • Wang, Z. and Guan, G. and Qiu, Y. and Zhuo, L. and Feng, D. Semantic context based refinement for news video annotation. Multimedia Tools and Applications, 2012.
  • Weston, J. and Blitzer, J. Latent Structured Ranking. Conference on Uncertainty in Artificial Intelligence, 2012.
  • Wohlkinger, W. and Aldoma, A. and Rusu, R.B. and Vincze, M. 3DNet: Large-Scale Object Class Recognition from CAD Models. Robotics and Automation (ICRA), 2012 IEEE International Conference on, 2012.
  • Yang, J. and Tian, Y. and Duan, L. and Huang, T. and Gao, W. Group-sensitive Multiple Kernel Learning for Object Recognition. Image Processing, IEEE Transactions on, 2012.
  • Yao, A. and Gall, J. and Leistner, C. and Van Gool, L. Interactive Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  • Yao, B. and Bradski, G. and Fei-Fei, L. A Codebook-Free and Annotation-Free Approach for Fine-Grained Image Categorization. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  • Zhao, Q. and Koch, C. Learning saliency-based visual attention: A review. Signal Processing, 2012.
  • Zhu, S. and Ngo, C. and Jiang, Y. Sampling and Ontologically Pooling Web Images for Visual Concept Learning. IEEE Transactions on Multimedia, 2012.
  • de Melo, G. and Weikum, G. Constructing and utilizing wordnets using statistical methods. Language Resources and Evaluation, 2012.
2011
  • Joly, A. and Buisson, O. Random Maximum Margin Hashing. CVPR, 2011
  • Özcan, M. and Jie23, L. and Ferrari, V. and Caputo, B. A large-scale database of images and captions for automatic face naming. Proceedings of the British Machine Vision Conference (BMVC), 2011.
  • Aytar, Y. and Zisserman, A. Tabula rasa: Model transfer for object category detection. IEEE International Conference on Computer Vision, 2011
  • Bakhtiari, A.S. and Bouguila, N. An Expandable Hierarchical Statistical Framework for Count Data Modeling and Its Application to Object Classification. Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference, 2011.
  • Bannour, H. and Hudelot, C. Towards ontologies for image interpretation and annotation. Content-Based Multimedia Indexing (CBMI), 2011 9th International Workshop on, 2011.
  • Bergamo, A. and Torresani, L. and Fitzgibbon, A. PICODES: Learning a Compact Code for Novel-Category Recognition. Neural Information Processing Systems (NIPS), 2011.
  • Bergsma, S. and Van Durme, B. Learning bilingual lexicons using the visual similarity of labeled web images. Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 2011.
  • Bo, L. and Lai, K. and Ren, X. and Fox, D. Object recognition with hierarchical kernel descriptors. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Bo, L. and Ren, X. and Fox, D. Depth kernel descriptors for object recognition. Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference, 2011.
  • Borth, D. and Ulges, A. and Breuel, T.M. Automatic concept-to-query mapping for web-based concept detector training. Proceedings of the 19th ACM international conference on Multimedia, 2011.
  • Borth, D. and Ulges, A. and Breuel, T.M. Lookapp: interactive construction of web-based concept detectors. Proceedings of the 1st ACM International Conference on Multimedia Retrieval, 2011.
  • Budikova, P. and Batko, M. and Zezula, P. Evaluation platform for content-based image retrieval systems. Research and Advanced Technology for Digital Libraries, 2011.
  • Cao, L. and Qi, G.J. and Tsai, S.F. and Tsai, M.H. and Pozo, A.D. and Huang, T.S. and Zhang, X. and Lim, S.H. Multimedia Information Networks in Social Media. Social Network Data Analytics, 2011.
  • Chandrasekhar, V. and Chen, D. and Tsai, S. and Cheung, N.M. and Chen, H. and Takacs, G. and Reznik, Y. and Vedantham, R. and Grzeszczuk, R. and Bach, J. and others. The stanford mobile visual search dataset. ACM Multimedia Systems Conference, 2011.
  • Chang, Y.J. and Chen, T. Semi-supervised learning with kernel locality-constrained linear coding. Image Processing (ICIP), 2011 18th IEEE International Conference, 2011.
  • Chitta, R. and Jin, R. and Havens, T.C. and Jain, A.K. Approximate kernel k-means: solution to large scale kernel clustering. Proc. ACM SIGKDD, 2011.
  • Choi, M. and Torralba, A. and Willsky, A. A Tree-Based Context Model for Object Recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2011.
  • Costea, A.D. and Varga, R. and Marita, T. and Nedevschi, S. Refining object recognition using scene specific object appearance frequencies. Intelligent Computer Communication and Processing (ICCP), 2011 IEEE International Conference, 2011.
  • Crouzet, S.M. and Serre, T. Frontiers: What are the Visual Features Underlying Rapid Object Recognition?. Frontiers in Perception Science, 2011.
  • Crouzet, S.M. and Serre, T. What are the visual features underlying rapid object recognition?. Frontiers in Psychology, 2011.
  • Deng, J. Feature Analysis for Object and Scene Categorization. Innovations in Intelligent Image Analysis, 2011.
  • Deng, J. and Berg, A.C. and Fei-Fei, L. Hierarchical semantic indexing for large scale image retrieval. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Deng, J. and Satheesh, S. and Berg, A.C. and Fei-Fei, L. Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition. Proceedings of the Neural Information Processing Systems (NIPS), 2011.
  • Deselaers, T. and Ferrari, V. Visual and semantic similarity in imagenet. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Donahue, J. and Grauman, K. Annotator rationales for visual recognition. Computer Vision (ICCV), 2011 IEEE International Conference, 2011.
  • Drauschke, M. and Förstner, W. A Bayesian approach for scene interpretation with integrated hierarchical structure. Pattern Recognition, 2011.
  • Duvenaud, D. and Marlin, B. and Murphy, K. Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification. Computer and Robot Vision (CRV), 2011 Canadian Conference, 2011.
  • Fan, J. and Shen, Y. and Yang, C. and Zhou, N. Structured max-margin learning for inter-related classifier training and multilabel image annotation. Image Processing, IEEE Transactions on, 2011.
  • Ferrari, M.O.J.L.V. and Caputo, B. TROPE. IDIAP Research Report, 2011
  • Fišer, D. and Novak, J. Visualizing sloWNet. Proceedings of eLex, 2011.
  • Fröhlich, B. and Rodner, E. and Kemmler, M. and Denzler, J. Efficient Gaussian process classification using random decision forests. Pattern Recognition and Image Analysis, 2011.
  • Gao, T. and Koller, D. Discriminative learning of relaxed hierarchy for large-scale visual recognition. Computer Vision (ICCV), 2011 IEEE International Conference, 2011.
  • Gao, T. and Koller, D. Multiclass Boosting with Hinge Loss based on Output Coding. Proceedings of the International Conference on Machine Learning (ICML), 2011.
  • García-Silva, A. and Jakob, M. and Mendes, P.N. and Bizer, C. Multipedia: enriching DBpedia with multimedia information. Informatica, 2011.
  • Gong, Y. and Lazebnik, S. Comparing data-dependent and data-independent embeddings for classification and ranking of Internet images. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Grauman, K. and Leibe, B. Visual Object Recognition. Morgan & Claypool, 2011.
  • Hu, D. and Bo, L. Toward Robust Material Recognition for Everyday Objects. Proceedings of the British Machine Vision Conference, 2011.
  • Huang, S. and Jin, L. and Wei, X. Online Heterogeneous Feature Fusion for Visual Recognition. Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference, 2011.
  • Huang, T.J. and Tian, Y.H. and Li, J. and Yu, H.N. Salient region detection and segmentation for general object recognition and image understanding. SCIENCE CHINA Information Sciences, 2011.
  • Hwang, S.J. and Grauman, K. Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search. International Journal of Computer Vision, 2011.
  • Hwang, S.J. and Grauman, K. and Sha, F. Learning a Tree of Metrics with Disjoint Visual Features. Advances in Neural Information Processing Systems (NIPS), 2011.
  • Jain, R. and Sankar, K.P. and Jawahar, CV Interpolation Based Tracking for Fast Object Detection in Videos. Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2011 Third National Conference, 2011.
  • James, N. and Todorov, K. and Hudelot, C. Combining visual and textual modalities for multimedia ontology matching. Semantic Multimedia, 2011.
  • Ji, R. and Duan, L.Y. and Chen, J. and Yang, S. and Huang, T. and Yao, H. and Gao, W. PKUBench: A context rich mobile visual search benchmark. Image Processing (ICIP), 2011 18th IEEE International Conference, 2011.
  • Jiang, Y.G. and Ye, G. and Chang, S.F. and Ellis, D. and Loui, A.C. Consumer video understanding: A benchmark database and an evaluation of human and machine performance. Proceedings of the 1st ACM International Conference on Multimedia Retrieval, 2011.
  • Jie, Luo. Open-Ended Learning of Visual and Multi-Modal Patterns. Thesis, 2011.
  • Johnson, S. and Everingham, M. Learning effective human pose estimation from inaccurate annotation. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Joshi, A.J. Image classification with minimal supervision. Dissertation, 2011.
  • Kanich, C. and Checkoway, S. and Mowery, K. Putting out a HIT: crowdsourcing malware installs. Proceedings of the 5th USENIX Workshop on Offensive Technologies, 2011.
  • Katti, H. Human Visual Perception, a study and applications to understanding Images and Videos. PhD Thesis, 2011.
  • Kesorn, K. and Poslad, S. An Enhanced Bag of Visual Word Vector Space Model to Represent Visual Content in Athletics Images. Multimedia, IEEE Transactions on, 2011.
  • Khamkar, A.K.B.S.S. and Kraut, R.E. CrowdForge: Crowdsourcing Complex Work. Proceedings of the 24th annual ACM symposium on User interface software and technology, 2011.
  • Kittur, A. and Smus, B. and Khamkar, S. and Kraut, R.E. Crowdforge: Crowdsourcing complex work. Proceedings of the 24th annual ACM symposium on User interface software and technology, 2011.
  • Kovashka, A. and Vijayanarasimhan, S. and Grauman, K. Actively selecting annotations among objects and attributes. Computer Vision (ICCV), 2011 IEEE International Conference, 2011.
  • Kuehne, H. and Jhuang, H. and Garrote, E. and Poggio, T. and Serre, T. HMDB: A large video database for human motion recognition. Computer Vision (ICCV), 2011 IEEE International Conference, 2011.
  • Kulkarni, G. and Premraj, V. and Dhar, S. and Li, S. and Choi, Y. and Berg, A.C. and Berg, T.L. Baby talk: Understanding and generating simple image descriptions. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Kumar, M.P. and Turki, H. and Preston, D. and Koller, D. Learning specific-class segmentation from diverse data. Computer Vision (ICCV), 2011 IEEE International Conference, 2011.
  • Kumar, N. and Berg, A. and Belhumeur, P. and Nayar, S. Describable visual attributes for face verification and image search. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2011.
  • Lai, K. and Bo, L. and Ren, X. and Fox, D. A large-scale hierarchical multi-view rgb-d object dataset. Robotics and Automation (ICRA), 2011 IEEE International Conference, 2011.
  • Lai, K. and Bo, L. and Ren, X. and Fox, D. A scalable tree-based approach for joint object and pose recognition. Twenty-Fifth Conference on Artificial Intelligence (AAAI), 2011.
  • Lai, K. and Bo, L. and Ren, X. and Fox, D. Sparse distance learning for object recognition combining RGB and depth information. Robotics and Automation (ICRA), 2011 IEEE International Conference, 2011.
  • Lara, A. and Hirata, R. Combining features to a class-specific model in an instance detection framework. Graphics, Patterns and Images (Sibgrapi), 2011 24th SIBGRAPI Conference, 2011.
  • Le, Q.V. and Monga, R. and Devin, M. and Corrado, G. and Chen, K. and Ranzato, M.A. and Dean, J. and Ng, A.Y. Building high-level features using large scale unsupervised learning. Arxiv preprint arXiv:1112.6209, 2011.
  • Leong, C.W. and Mihalcea, R. Going Beyond Text: A Hybrid Image-Text Approach for Measuring Word Relatedness. Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP), 2011.
  • Leong, C.W. and Mihalcea, R. Measuring the semantic relatedness between words and images. Proceedings of International Conference on Computational Semantics, 2011.
  • Li, J. and Wang, T. and Zhang, Y. Face detection using SURF cascade. Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference, 2011.
  • Li, L. and Jiang, S. and Huang, Q. Learning image vicept description via mixed-norm regularization for large scale semantic image search. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Li, L. and Jiang, S. and Huang, Q. Online Vicept learning for web-scale image understanding. Image Processing (ICIP), 2011 18th IEEE International Conference, 2011.
  • Li, P. and Guo, Y. and Sun, H. Multi-keyframe abstraction from videos. Image Processing (ICIP), 2011 18th IEEE International Conference, 2011.
  • Li, S. and Kulkarni, G. and Berg, T.L. and Berg, A.C. and Choi, Y. Composing simple image descriptions using web-scale n-grams. Proceedings of the Fifteenth Conference on Computational Natural Language Learning, 2011.
  • Li, X. and Snoek, C.G.M. and Worring, M. and Smeulders, A.W.M. Social negative bootstrapping for visual categorization. Proceedings of the 1st ACM International Conference on Multimedia Retrieval, 2011.
  • Li, Y. and Geng, B. and Zha, Z. and Li, Y. and Tao, D. and Xu, C. Query expansion by spatial co-occurrence for image retrieval. Proceedings of the 19th ACM international conference on Multimedia, 2011.
  • Li, Y. and Geng, B. and Zha, Z.J. and Tao, D. and Yang, L. and Xu, C. Difficulty guided image retrieval using linear multiview embedding. Proceedings of the 19th ACM international conference on Multimedia, 2011.
  • Li, Y. and Geng, B. and Zhou, C. and Xu, C. Learning to Combine Ad-hoc Ranking Functions for Image Retrieval. Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference, 2011.
  • Lim, E.H.Y. and Liu, J.N.K. and Lee, R.S.T. Knowledge Seeker-Ontology Modelling for Information Search and Management: A Compendium. Springer-Verlag New York Inc, 2011.
  • Liu, S. and Bai, X. Discriminative Features for Image Classification and Retrieval. Image and Graphics (ICIG), 2011 Sixth International Conference, 2011.
  • Liu, X. and Yao, H. and Ji, R. and Xu, P. and Sun, X. and Tian, Q. Learning heterogeneous data for hierarchical web video classification. Proceedings of the 19th ACM international conference on Multimedia, 2011.
  • Lu, Y. and Sebe, N. and Hytnen, R. and Tian, Q. Personalization in multimedia retrieval: A survey. Multimedia Tools and Applications, 2011.
  • McAuley, J. and Ramisa, A. and Caetano, T. Optimization of robust loss functions for weakly-labeled image taxonomies: an imagenet case study. Energy Minimazation Methods in Computer Vision and Pattern Recognition, 2011.
  • McCann, S. and Lowe, D.G. Local Naive Bayes Nearest Neighbor for Image Classification. Arxiv preprint arXiv:1112.0059, 2011.
  • Nakayama, H. Linear Distance Metric Learning for Large-scale Generic Image Recognition. Ph.D. Thesis, 2011
  • O'Hara, S. and Draper, B.A. Introduction to the bag of features paradigm for image classification and retrieval. Arxiv preprint arXiv:1101.3354, 2011.
  • Pastra, K. and Balta, E. and Dimitrakis, P. and Karakatsiotis, G. Embodied Language Processing: A New gGeneration of Language Technology. Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011.
  • Popescu, A. and Grefenstette, G. Social media driven image retrieval. Proceedings of the 1st ACM International Conference on Multimedia Retrieval, 2011.
  • Ranzato, M.A. and Susskind, J. and Mnih, V. and Hinton, G. On deep generative models with applications to recognition. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Rastegari, M. and Fang, C. and Torresani, L. Scalable Object-Class Search via Sparse Retrieval Models and Approximate Ranking. Dartmouth College Computer Science Technical Report series.
  • Rastegari, M. and Fang, C. and Torresani, L. Scalable object-class retrieval with approximate and top-k ranking. Computer Vision (ICCV), 2011 IEEE International Conference, 2011.
  • Ren, R. and Collomosse, J. and Jose, J. A BOVW based query generative model. Advances in Multimedia Modeling, 2011.
  • Rodner, E. Learning from Few Examples for Visual Recognition Problems. Hut Verlag München, 2011.
  • Rohrbach, M. and Stark, M. and Schiele, B. Evaluating knowledge transfer and zero-shot learning in a large-scale setting. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Sánchez, J. and Perronnin, F. High-dimensional signature compression for large-scale image classification. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • S.M. Crouzet & T. Serre. What are the visual features underlying rapid recognition? Front. in Perception Science (Special issue on the "The timing of visual recognition"), 2011.
  • Savage, N. Sorting through photos. Communications of the ACM, 2011.
  • Shimada, A. and Nagahara, H. and Taniguchi, R. and Charvillat, V. Geolocation based image annotation. Pattern Recognition (ACPR), 2011 First Asian Conference, 2011.
  • Steggink, J. and Snoek, C.G.M. Adding semantics to image-region annotations with the Name-It-Game. Multimedia Systems, 2011.
  • Suditu, N. and Fleuret, F. HEAT: Iterative relevance feedback with one million images. Computer Vision (ICCV), 2011 IEEE International Conference, 2011.
  • Tacchetti, A. and Mallapragada, P. and Santoro, M. and Rosasco, L. GURLS: A toolbox for large scale multiclass learning. Workshop: "Big Learning: Algorithms, Systems, and Tools for Learning at Scale" at Neural Information Processing Systems (NIPS), 2011.
  • Taneva, B. and Kacimi, M. and Weikum, G. Finding images of difficult entities in the long tail. Proceedings of the 20th ACM international conference on Information and knowledge management, 2011.
  • Tatu, A. and Lauze, F. and Nielsen, M. and Kimia, B. Exploring the representation capabilities of the HOG descriptor. Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference, 2011.
  • Taylor, G.W. and Spiro, I. and Bregler, C. and Fergus, R. Learning invariance through imitation. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Theodosiou, Z. and Georgiou, O. and Tsapatsoulis, N. Evaluating Annotators Consistency with the Aid of an Innovative Database Schema. Semantic Media Adaptation and Personalization (SMAP), 2011 Sixth International Workshop on, 2011.
  • Todorov, K. and James, N. and Hudelot, C. Multimedia ontology matching by using visual and textual modalities. Multimedia Tools and Applications, 2011.
  • Tousch, A.M. and Herbin, S. and Audibert, J.Y. Semantic hierarchies for image annotation: A survey. Pattern Recognition, 2011.
  • Tsai, D. and Jing, Y. and Liu, Y. and Rowley, H.A. and Ioffe, S. and Rehg, J.M. Large-scale image annotation using visual synset. Computer Vision (ICCV), 2011 IEEE International Conference, 2011.
  • Tsai, S.F. and Huang, T.S. and Tang, F. Album-based object-centric event recognition. Multimedia and Expo (ICME), 2011 IEEE International Conference, 2011.
  • Uchida, S. and Cai, W. and Yoshida, A. and Feng, Y. Watching pattern distribution via massive character recognition. Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on, 2011.
  • Vicente Ordonez, Girish Kulkarni, Tamara L. Berg. Im2Text: Describing Images Using 1 Million Captioned Photographs. Neural Information Processing Systems (NIPS), 2011.
  • Vijayanarasimhan, S. and Grauman, K. Large-scale live active learning: training object detectors with crawled data and crowds. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Vittayakorn, S. and Hays, J. Quality Assessment for Crowdsourced Object Annotations. Jesse Hoey, Stephen McKenna and Emanuele Trucco, Proceedings of the British Machine Vision Conference, pages, 2011.
  • WANG, C. and Jun, MA Rank Refinement for Social Images by a Random Walk Model. Journal of Computational Information Systems, 2011.
  • Wah, C. Crowdsourcing and Its Applications in Computer Vision. UCSD CSE Research Exam, 2011.
  • Wah, C. and Branson, S. and Welinder, P. and Perona, P. and Belongie, S. The Caltech-UCSD Birds-200-2011 Dataset. California Institute of Technology, 2011.
  • Wang, H. and Zhang, S. Evaluation of global descriptors for large scale image retrieval. Image Analysis and Processing–ICIAP 2011, 2011.
  • Wang, J. and Ji, L. and Wang, L. and Gao, L. and Shen, Z. A sharing data platform for insect image researches. Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference, 2011.
  • Wang, X. and Yang, M. and Cour, T. and Zhu, S. and Yu, K. and Han, T.X. Contextual weighting for vocabulary tree based image retrieval. Computer Vision (ICCV), 2011 IEEE International Conference, 2011.
  • Wang, X. and Yang, M. and Yu, K. Efficient Re-ranking in Vocabulary Tree based Image Retrieval. 45th Asilomar Conference on Signals, Systems and Computers, 2011
  • Wang, Y. and Mori, G. Max-Margin Latent Dirichlet Allocation for Image Classification and Annotation. 22nd British Machine Vision Conference (BMVC), 2011.
  • Weikum, G. and Bedathur, S. and Schenkel, R. Temporal knowledge for timely intelligence. Enabling Real-Time Business Intelligence, 2011.
  • Weston, J. and Bengio, S. and Usunier, N. Wsabie: Scaling up to large vocabulary image annotation. Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI–11), 2011.
  • Wu, L. and Hua, X. and Yu, N. and Ma, W. and Li, S. Flickr Distance: A Relationship Measure for Visual Concepts. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2011.
  • Wu, Y. and Liu, Y. and Yuan, Z. and Zheng, N. IAIR-CarPed: A psychophysically annotated dataset with fine-grained and layered semantic labels for object recognition. Pattern Recognition Letters, 2011.
  • Xie, Z. and Gao, J. and Wu, K. and Zhang, J. Brief survey on image semantic analysis and understanding. Soft Computing and Pattern Recognition (SoCPaR), 2011 International Conference of, 2011.
  • Yang, K. and Wang, M. and Hua, X.S. and Yan, S. and Zhang, H.J. Assemble new object detector with few examples. Image Processing, IEEE Transactions on, 2011.
  • Yang, K. and Zhang, L. and Wang, M. and Zhang, H.J. Semantic point detector. Proceedings of the 19th ACM international conference on Multimedia, 2011.
  • Yang, L. and Geng, B. and Cai, Y. and Hanjalic, A. and Hua, X. Object Retrieval using Visual Query Context. Multimedia, IEEE Transactions on, 2011.
  • Yang, W. and Toderici, G. Discriminative Tag Learning on YouTube Videos with Latent Sub-tags. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
  • Yang, W. and Toderici, G. Discriminative tag learning on YouTube videos with latent sub-tags. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 2011.
  • Yao, B. and Jiang, X. and Khosla, A. and Lin, A.L. and Guibas, L. and Fei-Fei, L. Human action recognition by learning bases of action attributes and parts. Computer Vision (ICCV), 2011 IEEE International Conference, 2011.
  • Zhang, H.J. Assemble New Object Detector with Few Examples. IEEE Transactions on Image Processing, 2011.
  • Zhang, J. and Yu, Y. and Zheng, S. and Huang, K. An Empirical Study of Visual Features for Part Based Model. Asian Conference on Pattern Recognition (ACPR), 2011.
  • Zhang, L. and Ma, J. and Cui, C. and Li, P. Active learning through notes data in Flickr: an effortless training data acquisition approach for object localization. Proceedings of the 1st ACM International Conference on Multimedia Retrieval, 2011.
  • Zhang, N. and Mei, T. and Hua, X.S. and Guan, L. and Li, S. Tap-to-search: Interactive and contextual visual search on mobile devices. Multimedia Signal Processing (MMSP), 2011 IEEE 13th International Workshop on, 2011.
  • Zhang, S. and Tian, Q. and Hua, G. and Huang, Q. and Gao, W. Generating Descriptive Visual Words and Visual Phrases for Large-Scale Image Applications. Image Processing, IEEE Transactions on, 2011.
  • Zhang, S. and Tian, Q. and Hua, G. and Zhou, W. and Huang, Q. and Li, H. and Gao, W. Modeling spatial and semantic cues for large-scale near-duplicated image retrieval. Computer Vision and Image Understanding, 2011.
  • Zhang, S. and Tian, Q. and Huang, Q. and Gao, W. ObjectBook construction for large-scale semantic-aware image retrieval. Multimedia Signal Processing (MMSP), 2011 IEEE 13th International Workshop on, 2011.
  • Zhang, X. and Zha, Z.J. and Xu, C. Learning verb-object concepts for semantic image annotation. Proceedings of the 19th ACM international conference on Multimedia, 2011.
  • Zhang, X. and Zhang, L. and Wang, XJ and Shum, H. Finding Celebrities in Billions of Web Images. Multimedia, IEEE Transactions on, 2011.
  • Zhao, B. and Fei-Fei, L. and Xing, E.P. Large-Scale Category Structure Aware Image Categorization. Proceedings of the Neural Information Processing Systems (NIPS). 2011.
  • von Wyl, M. and Mohamed, H. and Bruno, E. and Marchand-Maillet, S. A parallel cross-modal search engine over large-scale multimedia collections with interactive relevance feedback. Proceedings of the 1st ACM International Conference on Multimedia Retrieval, 2011.
2010
  • Abbott, J.T. Relevance Feedback and Novelty Detection under the Bayesian Sets Framework. Master's thesis, 2010.
  • Baba, Y. and Honiden, S. Automatically Mapping Flickr Images to WordNet. The 24th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI 2010), 2010.
  • Behmo, R. Visual feature graphs and image recognition. PhD Thesis, 2010.
  • Bengio, S. and Weston, J. and Grangier, D. Label embedding trees for large multi-class tasks. Advances in Neural Information Processing Systems, 2010.
  • Berg, T.L. and Sorokin, A. and Wang, G. and Forsyth, D.A. and Hoiem, D. and Endres, I. and Farhadi, A. It's all about the data. Proceedings of the IEEE, 2010.
  • Bergamo, A. and Torresani, L. Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach. Advances in Neural Information Processing Systems (NIPS), 2010.
  • Bo, L. and Ren, X. and Fox, D. Kernel descriptors for visual recognition. Advances in Neural Information Processing Systems, 2010.
  • Callison-Burch, C. and Dredze, M. Creating speech and language data with Amazon's Mechanical Turk. Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk, 2010.
  • Chen, S. and Zhang, J. and Chen, G. and Zhang, C. What if the Irresponsible Teachers Are Dominating? A Method of Training on Samples and Clustering on Teachers. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10), 2010.
  • Chen, X. and Hu, X. and Zhou, Z. and Lu, C. and Rosen, G. and He, T. and Park, EK A probabilistic topic-connection model for automatic image annotation. Proceedings of the 19th ACM international conference on Information and knowledge management, 2010.
  • Davis, J. and Arderiu, J. and Lin, H. and Nevins, Z. and Schuon, S. and Gallo, O. and Yang, M.H. The HPU. Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference, 2010.
  • De Melo, G. and Weikum, G. Providing multilingual, multimodal answers to lexical database queries. Proceedings of the 7th International Conference on Language Resources and Evaluation, 2010.
  • Deng, J. and Berg, A. and Li, K. and Fei-Fei, L. What does classifying more than 10,000 image categories tell us?. Computer Vision–ECCV 2010, 2010.
  • Duchi, J. and Hazan, E. and Singer, Y. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 2010.
  • Duvenaud, D. Multiscale conditional random fields for machine vision. University of British Columbia, 2010.
  • Farhadi, A. and Endres, I. and Hoiem, D. Attribute-centric recognition for cross-category generalization. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, 2010.
  • Feng, J. and Zheng, Y. and Yan, S. Towards a universal detector by mining concepts with small semantic gaps. Proceedings of the international conference on Multimedia, 2010.
  • Fergus, R. and Bernal, H. and Weiss, Y. and Torralba, A. Semantic label sharing for learning with many categories. Computer Vision–ECCV 2010, 2010.
  • Fu, Z. and Lu, H. and Deng, N. and Cai, N. Large scale visual classification via learned dictionaries and sparse representation. Artificial Intelligence and Computational Intelligence, 2010.
  • Fujita, S. and Nagata, M. Enriching dictionaries with images from the internet: targeting Wikipedia and a Japanese semantic lexicon: Lexeed. Proceedings of the 23rd International Conference on Computational Linguistics, 2010.
  • Gould, S. Probabilistic Models for Region-based Scene Understanding. PhD Thesis, 2010.
  • Guillaumin, M. and Verbeek, J. and Schmid, C. Multimodal semi-supervised learning for image classification. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, 2010.
  • Hayashi, Y. and Nagata, M. and Savas, B. Exploring the Visual Annotatability of Query Concepts for Interactive Cross-Language Information Retrieval. Information Retrieval Technology, 2010.
  • Hedlund, T. and Yasar, T. Publishing in the Networked World: Transforming the Nature of Communication, 14th International Conference on Electronic Publishing 16-18 June 2010, Helsinki, Finland. Hanken School of Economics, 2010.
  • Huang, E.H.C. Automatic Task Design on Amazon Mechanical Turk. Thesis, 2010.
  • Hwang, S.J. and Grauman, K. Accounting for the relative importance of objects in image retrieval. Proceedings of the British Machine Vision Conference, pages, 2010.
  • Hwang, S.J. and Grauman, K. Reading between the lines: Object localization using implicit cues from image tags. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, 2010.
  • James, N. and Todorov, K. and Hudelot, C. Enabling Interoperability between Multimedia Resources: An Ontology Matching Perspective. 11th International Workshop of the Multimedia Metadata Community, Workshop on Interoperable Social Multimedia Applications, 2010.
  • James, N. and Todorov, K. and Hudelot, C. Ontology matching for the semantic annotation of images. Fuzzy Systems (FUZZ), 2010 IEEE International Conference, 2010.
  • Joshi, A.J. and Porikli, F. and Papanikolopoulos, N. Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, 2010.
  • Kawakubo, H. and Akima, Y. and Yanai, K. Automatic Construction of a Folksonomy-Based Visual Ontology. Multimedia (ISM), 2010 IEEE International Symposium on, 2010.
  • Li, L.J. and Su, H. and Lim, Y. and Fei-Fei, L. Objects as attributes for scene classification. ECCV First International Workshop on Parts and Attributes, 2010.
  • Li, L.J. and Su, H. and Xing, E.P. and Fei-Fei, L. Object bank: A high-level image representation for scene classification and semantic feature sparsification. Advances in Neural Information Processing Systems, 2010.
  • Llorente, A. Semantics and statistics for automated image annotation. PhD Thesis, 2010.
  • Ma, X. and Fellbaum, C. and Cook, P.R. A multimodal vocabulary for augmentative and alternative communication from sound/image label datasets. Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies, 2010.
  • Maly, K. and Wu, H. and Zubair, M. A collaborative faceted categorization system-user interactions. Publishing in the networked world: transforming the nature of communication, 2010.
  • Marée, R. and Denis, P. and Wehenkel, L. and Geurts, P. Incremental indexing and distributed image search using shared randomized vocabularies. Proceedings of the international conference on Multimedia information retrieval, 2010.
  • Meger, D. and Muja, M. and Helmer, S. and Gupta, A. and Gamroth, C. and Hoffman, T. and Baumann, M. and Southey, T. and Fazli, P. and Wohlkinger, W. and others. Curious George: An integrated visual search platform. Computer and Robot Vision (CRV), 2010 Canadian Conference, 2010.
  • Nikolova, S. Improving word-finding in assistive communication tools: A mixed-initiative approach. Dissertation, 2010.
  • Nowak, S. and Rüger, S. How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation. Proceedings of the international conference on Multimedia information retrieval, 2010.
  • Parikh, D. and Zitnick, C.L. The role of features, algorithms and data in visual recognition. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, 2010.
  • Perona, P. Vision of a Visipedia. Proceedings of the IEEE, 2010.
  • Perronnin, F. and Sánchez, J. and Mensink, T. Improving the fisher kernel for large-scale image classification. Computer Vision–ECCV 2010, 2010.
  • Perronnin, F. and Senchez, J. and others Large-scale image categorization with explicit data embedding. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, 2010.
  • Ross, J. and Irani, L. and Silberman, M. and Zaldivar, A. and Tomlinson, B. Who are the crowdworkers?: shifting demographics in mechanical turk. Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems, 2010.
  • Russakovsky, O. and Fei-Fei, L. Attribute learning in large-scale datasets. ECCV 2010 Workshop on Parts and Attributes, 2010.
  • Shalit, U. and Weinshall, D. and Chechik, G. Online learning in the manifold of low-rank matrices. Advances in Neural Information Processing Systems, 2010.
  • Siddiquie, B. and Gupta, A. Beyond active noun tagging: Modeling contextual interactions for multi-class active learning. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, 2010.
  • Solli, M. and Lenz, R. Emotion related structures in large image databases. Proceedings of the ACM International Conference on Image and Video Retrieval, 2010.
  • Song, Y. and Zhao, M. and Yagnik, J. and Wu, X. Taxonomic classification for web-based videos. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, 2010.
  • Su, S.Z. and ZiLi, S. and Chen, S.Y. and Li, S.A. and Duh, D.J. Structured Local Binary Haar pattern for graphics retrieval. Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference, 2010.
  • Taneva, B. and Kacimi, M. and Weikum, G. Gathering and ranking photos of named entities with high precision, high recall, and diversity. Proceedings of the third ACM international conference on Web search and data mining, 2010.
  • Torralba, A. and Russell, B.C. and Yuen, J. LabelMe: online image annotation and applications. Proceedings of the IEEE, 2010.
  • Vijayanarasimhan, S. and Jain, P. and Grauman, K. Far-sighted active learning on a budget for image and video recognition. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, 2010.
  • Vondrick, C. and Ramanan, D. and Patterson, D. Efficiently scaling up video annotation with crowdsourced marketplaces. Computer Vision–ECCV 2010, 2010.
  • Wang, S. and Huang, Q. and Jiang, S. and Tian, Q. Nearest-neighbor classification using unlabeled data for real world image application. Proceedings of the international conference on Multimedia, 2010.
  • Wang, S. and Jiang, S. and Huang, Q. and Tian, Q. S3MKL: scalable semi-supervised multiple kernel learning for image data mining. Proceedings of the international conference on Multimedia, 2010.
  • Wang, X.J. and Zhang, L. and Liu, M. and Li, Y. and Ma, W.Y. Arista-image search to annotation on billions of web photos. Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, 2010.
  • Wang, Z. and Xia, D. and Chang, E.Y. A deep-learning model-based and data-driven hybrid architecture for image annotation. Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval, 2010.
  • Welinder, P. and Branson, S. and Belongie, S. and Perona, P. The multidimensional wisdom of crowds. Neural Information Processing Systems Conference (NIPS), 2010.
  • Welinder, P. and Branson, S. and Mita, T. and Wah, C. and Schroff, F. and Belongie, S. and Perona, P. Caltech-UCSD birds 200. California Institute of Technology, 2010.
  • Weston, J. and Bengio, S. and Usunier, N. Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 2010.
  • Wu, Z. and Jiang, S. and Li, L. and Cui, P. and Huang, Q. and Gao, W. Vicept: link visual features to concepts for large-scale image understanding. Proceedings of the international conference on Multimedia, 2010.
  • Xue, X. and Luo, H. and Fan, J. Structured max-margin learning for multi-label image annotation. Proceedings of the ACM International Conference on Image and Video Retrieval, 2010.
  • Yang, L. and Geng, B. and Hanjalic, A. and Hua, X.S. Contextual image retrieval model. Proceedings of the ACM International Conference on Image and Video Retrieval, 2010.
  • Yao, B.Z. and Yang, X. and Lin, L. and Lee, M.W. and Zhu, S.C. I2t: Image parsing to text description. Proceedings of the IEEE, 2010.
  • Yong, S.P. and Deng, J.D. and Purvis, M.K. Modelling semantic context for novelty detection in wildlife scenes. Multimedia and Expo (ICME), 2010 IEEE International Conference, 2010.
  • Yu, K. and Wang, Z. and Zhuo, L. and Feng, D. Harvesting Web Images for Realistic Facial Expression Recognition. Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference, 2010.
  • Zhang, J. and Liu, X. and Luo, J. and Lang, B. DIRS: Distributed image retrieval system based on MapReduce. Pervasive Computing and Applications (ICPCA), 2010 5th International Conference, 2010.
  • Zhang, S. and Huang, Q. and Hua, G. and Jiang, S. and Gao, W. and Tian, Q. Building contextual visual vocabulary for large-scale image applications. Proceedings of the international conference on Multimedia, 2010.
  • Zhou, W. and Lu, Y. and Li, H. and Song, Y. and Tian, Q. Spatial coding for large scale partial-duplicate web image search. Proceedings of the international conference on Multimedia, 2010.
2009
  • Adelson, E.H. and Sharan, L. and others The perception of material qualities in real-world images. Ph.D. Thesis, 2009.
  • Boyd-Graber, J. and Chang, J. and Gerrish, S. and Wang, C. and Blei, D. Reading tea leaves: How humans interpret topic models. Proceedings of the 23rd Annual Conference on Neural Information Processing Systems, 2009.
  • Chua, T.S. and Tang, S. and Trichet, R. and Tan, H.K. and Song, Y. Moviebase: a movie database for event detection and behavioral analysis. Proceedings of the 1st workshop on Web-scale multimedia corpus, 2009.
  • Hussein, M.E.A. Algorithmic issues in visual object recognition. Dissertation, 2009.
  • Li, H. and Wang, M. and Hua, X.S. MSRA-MM 2.0: A large-scale web multimedia dataset. Data Mining Workshops, 2009. ICDMW'09. IEEE International Conference, 2009.
  • Ma, X. and Boyd-Graber, J. and Nikolova, S. and Cook, P.R. Speaking through pictures: Images vs. icons. Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility, 2009.
  • Ma, X. and Nikolova, S. and Cook, P.R. W2ANE: when words are not enough: online multimedia language assistant for people with aphasia. Proceedings of the 17th ACM international conference on Multimedia, 2009.
  • Maji, S. and Berg, A.C. Max-margin additive classifiers for detection. Computer Vision, 2009 IEEE 12th International Conference, 2009.
  • Nikolova, S. and Boyd-Graber, J. and Fellbaum, C. and Cook, P. Better vocabularies for assistive communication aids: connecting terms using semantic networks and untrained annotators. Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility, 2009.
  • Su, H. and Sun, M. and Fei-Fei, L. and Savarese, S. Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories. Computer Vision, 2009 IEEE 12th International Conference, 2009.
  • Zhang, S. and Tian, Q. and Hua, G. and Huang, Q. and Li, S. Descriptive visual words and visual phrases for image applications. Proceedings of the 17th ACM international conference on Multimedia, 2009.