Vector quantization and signal compression
Vector quantization and signal compression
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Constructing Category Hierarchies for Visual Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Product Quantization for Nearest Neighbor Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
CENTRIST: A Visual Descriptor for Scene Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Additive Kernels via Explicit Feature Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic representation for efficient large scale visual recognition tasks
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Empowering Visual Categorization With the GPU
IEEE Transactions on Multimedia
Power mean SVM for large scale visual classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Vector quantization (VQ) using exhaustive nearest neighbor (NN) search is the speed bottleneck in classic bag of visual words (BOV) models. Approximate NN (ANN) search methods still cost great time in VQ, since they check multiple regions in the search space to reduce VQ errors. In this paper, we propose ExVQ, an exclusive NN search method to speed up BOV models. Given a visual descriptor, a portion of search regions is excluded from the whole search space by a linear projection. We ensure that minimal VQ errors are introduced in the exclusion by learning an accurate classifier. Multiple exclusions are organized in a tree structure in ExVQ, whose VQ speed and VQ error rate can be reliably estimated. We show that ExVQ is much faster than state-of-the-art ANN methods in BOV models while maintaining almost the same classification accuracy. In addition, we empirically show that even with the VQ error rate as high as 30%, the classification accuracy of some ANN methods, including ExVQ, is similar to that of exhaustive search (which has zero VQ error). In some cases, ExVQ has even higher classification accuracy than the exhaustive search.