We currently provide densely sampled SIFT features. We provide raw SIFT descriptors as well as quantized codewords. Spatial coordiates of each descriptor/codeword are also included. The quantized codewords are suitable for Bag of Words representations. The features are packaged as Matlab files and can be freely downloaded ( no signing-in is required ). Details are as follows:
- David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004. pdf
- L. Fei-Fei and P. Perona, A Bayesian Hierarchical Model for Learning Natural Scene Categories. IEEE Comp. Vis. Patt. Recog. 2005. pdf
- Svetlana Lazebnik, Cordelia Schmid and Jean Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. IEEE Comp. Vis. Patt. Recog. 2006. pdf
- A. Vedaldi and B. Fulkerson. VLFeat: An Open and Portable Library of Computer Vision Algorithms. 2008. http://www.vlfeat.org
How to download?
- We have not yet released SIFT features for all synsets. To check the list of synsets with SIFT features released, please use the API:
- When you browse ImageNet from the Explore page, you can download the bag of visual words (sbow) feature of a synset if there is an icon "Download BoW Feature" below the image view panel.
- You can also download the raw SIFT descriptors using the following API:
- You can download the bag of visual words ( sbow ) feature for a given synset using the API:
Code for computing the features
- The code used to computing the features has been released in the development kit of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC2010). Please consult the readme file in the kit for details.