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Seminar abstract

Exclusive Visual Descriptor Quantization

Jianxin Wu
Assistant Professor
School of Computer Engineering, Nanyang Technological University

Abstract: 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 talk, I will introduce 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.

Bio: Jianxin Wu received his BS & MS degree in computer science from the Nanjing University, and the PhD degree in computer science from the Georgia Institute of Technology. He is currently an assistant professor in the School of Computer Engineering, Nanyang Technological University, Singapore. His research interests are computer vision and machine learning. He is in particular interested in machine learning problems of large scale computer vision problems.

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