我們介紹一個關於向量量化與維度縮減的新觀察。此觀察能夠幫助改進近似最近鄰近搜索的品質。根據此觀察我們發展一個利用量化誤差與均衡子空間之方差準則的高效演算法。實驗結果顯示我們的方法在搜索大型資料組能夠達到更好的表現。我們也利用我們的快速近似最近鄰近搜索方法呈現一個應用。

We introduce a new observation about vector quantization and dimensionality reduction. This observation can help to improve the quality of approximate nearest neighbor search. Based on the observation we develop an efficient algorithm leveraging the quantization error and the balanced variances of subspaces criteria for codebook learning. Experimental results show that our approach is able to achieve better performance on searching large datasets. We also present an application that takes advantage of fast approximate nearest neighbor search with our approach.

[1] Artem Babenko and Victor S. Lempitsky. Additive quantization for extreme vector compression. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pages 931-938, 2014.
[2] Tali Dekel, Shaul Oron, Michael Rubinstein, Shai Avidan, and William T Freeman. Best-buddies similarity for robust template matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2021-2029, 2015.
[3] Tiezheng Ge, Kaiming He, Qifa Ke, and Jian Sun. Optimized product quantization. IEEE Trans. Pattern Anal. Mach. Intell., 36(4):744-755, 2014.
[4] Yunchao Gong, Sanjiv Kumar, Henry A. Rowley, and Svetlana Lazebnik. Learning binary codes for high-dimensional data using bilinear projections. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013, pages 484-491, 2013.
[5] Yunchao Gong, Svetlana Lazebnik, Albert Gordo, and Florent Perronnin. Iterative quantization: A procrustean approach to learning binary codes for largescale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell., 35(12):2916-2929, 2013.
[6] Herve Jegou, Matthijs Douze, and Cordelia Schmid. Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell., 33(1):117-128, 2011.
[7] Alex Krizhevsky. Learning multiple layers of features from tiny images, 2009.
[8] David G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
[9] Aude Oliva and Antonio Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145-175, 2001.
[10] 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. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), pages 1-42, April 2015.
[11] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556, 2014.
[12] A. Vedaldi and K. Lenc. Matconvnet { convolutional neural networks for matlab. CoRR, abs/1412.4564, 2014.
[13] C.Wah, S. Branson, P.Welinder, P. Perona, and S. Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical report, 2011.
[14] Ting Zhang, Chao Du, and Jingdong Wang. Composite quantization for approximate nearest neighbor search. In Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014, pages 838-846, 2014.
[15] C. Lawrence Zitnick and Piotr Dollar. Edge boxes: Locating object proposals from edges. In Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V, pages 391-405, 2014.