CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2014 vol.36 Issue No.01 - Jan.

Subscribe

Issue No.01 - Jan. (2014 vol.36)

pp: 33-47

Albert Gordo , LEAR Group, INRIA Grenoble Rhone-Alpes, Montbonnot, France

Florent Perronnin , Xerox Res. Centre Eur. (XRCE), Meylan, France

Yunchao Gong , Dept. of Comput. Sci., Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Svetlana Lazebnik , Dept. of Comput. Sci., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA

ABSTRACT

In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes that binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances that are applicable to a wide variety of embedding techniques including locality sensitive hashing (LSH), locality sensitive binary codes (LSBC), spectral hashing (SH), PCA embedding (PCAE), PCAE with random rotations (PCAE-RR), and PCAE with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.

INDEX TERMS

Principal component analysis, Euclidean distance, Vectors, Kernel, Matrix decomposition, Quantization (signal), Algorithm design and analysis,asymmetric distances, Large-scale retrieval, binary codes

CITATION

Albert Gordo, Florent Perronnin, Yunchao Gong, Svetlana Lazebnik, "Asymmetric Distances for Binary Embeddings",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.36, no. 1, pp. 33-47, Jan. 2014, doi:10.1109/TPAMI.2013.101REFERENCES

- [1] A. Gordo and F. Perronnin, "Asymmetric Distances for Binary Embeddings,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 729-736, 2011.- [2] M. Everingham, L.V. Gool, C. Williams, J. Winn, and A. Zisserman, "The Pascal Visual Object Classes (VOC) Challenge,"
Int'l J. Computer Vision, vol. 88, no. 2, pp. 303-338, 2010.- [3] L. Fei-Fei, R. Fergus, and P. Perona, "One-Shot Learning of Object Categories,"
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 594-611, Apr. 2006.- [4] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A Large-Scale Hierarchical Image Database,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 248-255, 2009.- [5] A. Torralba, R. Fergus, and W. Freeman, "80 Million Tiny Images: A Large Data Set for Non-Parametric Object and Scene Recognition,"
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1958-1970, Nov. 2008.- [6] A. Oliva and A. Torralba, "Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope,"
Int'l J. Computer Vision, vol. 42, pp. 145-175, 2001.- [7] J. Sivic and A. Zisserman, "Video Google: A Text Retrieval Approach to Object Matching in Videos,"
Proc. Ninth IEEE Int'l Conf. Computer Vision, pp. 1470-1477, 2003.- [8] G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, "Visual Categorization with Bags of Keypoints,"
Proc. Workshop Statistical Learning Computer Vision, 2004.- [9] F. Perronnin and C. Dance, "Fisher Kernels on Visual Vocabularies for Image Categorization,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2007.- [10] F. Perronnin, J. Sánchez, and T. Mensink, "Improving the Fisher Kernel for Large-Scale Image Classification,"
Proc. 11th European Conf. Computer Vision, pp. 143-156, 2010.- [11] H. Jégou, M. Douze, C. Schmid, and P. Pérez, "Aggregating Local Descriptors into a Compact Image Representation,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 3304-3311, 2010.- [12] P. Indyk and R. Motwani, "Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality,"
Proc. 13th Ann. ACM Symp. Theory Computing (STOC '98), pp. 604-613, 1998.- [13] M. Charikar, "Similarity Estimation Techniques from Rounding Algorithms,"
Proc. 34th Ann. ACM Symp. Theory Computing (STOC '02), pp. 380-388, 2002.- [14] M. Raginsky and S. Lazebnik, "Locality-Sensitive Binary Codes from Shift-Invariant Kernels,"
Proc. Advances in Neural Information Processing, pp. 1509-1517, 2009.- [15] Y. Weiss, A. Torralba, and R. Fergus, "Spectral Hashing,"
Proc. Advances in Neural Information Processing, 2008.- [16] B. Kulis and K. Grauman, "Kernelized Locality-Sensitive Hashing for Scalable Image Search,"
Proc. IEEE Int'l Conf. Computer Vision, pp. 2130-2137, 2009.- [17] J. Brandt, "Transform Coding for Fast Approximate Nearest Neighbor Search in High Dimensions,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1815-1822, 2010.- [18] J. Wang, S. Kumar, and S.-F. Chang, "Semi-Supervised Hashing for Large Scale Search,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.- [19] L. Torresani, M. Szummer, and A. Fitzgibbon, "Efficient Object Category Recognition Using Classemes,"
Proc. 11th European Conf. Computer Vision, pp. 776-789, 2010.- [20] A. Bergamo, L. Torresani, and A. Fitzgibbon, "PiCoDes: Learning a Compact Code for Novel-Category Recognition,"
Proc. Neural Information Processing Systems, pp. 2088-2096, 2011.- [21] S. Korman and S. Avidan, "Coherency Sensitive Hashing,"
Proc. IEEE Int'l Conf. Computer Vision, pp. 1607-1614, 2011.- [22] Y. Gong and S. Lazebnik, "Iterative Quantization: A Procrustean Approach to Learning Binary Codes,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 817-824, 2011.- [23] M. Norouzi and D. Fleet, "Minimal Loss Hashing for Compact Binary Codes,"
Proc. Int'l Conf. Machine Learning, 2011.- [24] J. Wang, S. Kumar, and S. Chang, "Sequential Projection Learning for Hashing with Compact Codes,"
Proc. Int'l Conf. Machine Learning, 2011.- [25] J.-P. Heo, Y. Lee, J. He, S.-F. Chang, and S. eui Yoon, "Spherical Hashing,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2957-2964, 2012.- [26] C. Strecha, A.M. Bronstein, M.M. Bronstein, and P. Fua, "LDAHash: Improved Matching with Smaller Descriptors,"
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 1, pp. 66-78, Jan. 2012.- [27] M. Norouzi, R. Salakhutdinov, and D. Fleet, "Hamming Distance Metric Learning,"
Proc. Advances in Neural Information Processing Systems, pp. 1070-1078, 2012.- [28] Y. Gong, S. Kumar, V. Verma, and S. Lazebnik, "Angular Quantization Based Binary Codes for Fast Similarity Search,"
Proc. Advances in Neural Information Processing Systems, pp. 1205-1213, 2012.- [29] W. Liu, J. Wang, R. Ji, Y.-G. Jiang, and S.-F. Chang, "Supervised Hashing with Kernels,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2074-2081, 2012.- [30] Y. Weiss, R. Fergus, and A. Torralba, "Multidimensional Spectral Hashing,"
Proc. 12th European Conf. Computer Vision (ECCV '12), pp. 340-353, 2012.- [31] W. Dong, M. Charikar, and K. Li, "Asymmetric Distance Estimation with Sketches for Similarity Search in High-Dimensional Spaces,"
Proc. 31st Ann. Int'l ACM SIGIR Conf. Research Development Information Retrieval (SIGIR '08), 2008.- [32] H. Jégou, M. Douze, and C. Schmid, "Product Quantization for Nearest Neighbor Search,"
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 117-128, Jan. 2010.- [33] A. Rahimi and B. Recht, "Random Features for Large-Scale Kernel Machines,"
Proc. Conf. Advances in Neural Information Processing Systems, 2007.- [34] H. Jégou, M. Douze, and C. Schmid, "Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search,"
Proc. 10th European Conf. Computer Vision, pp. 304-317, http://lear.inrialpes.fr/~jegoudata.php, 2008.- [35] P. Schonemann, "A Generalized Solution of the Orthogonal Procrustres Problem,"
Psychometrika, vol. 31, pp. 1-10, 1966.- [36] A. Krizhevsky, "Learning Multiple Layers of Features from Tiny Images," technical report, Univ. of Toronto, 2009.
- [37] G. Griffin, A. Holub, and P. Perona, "Caltech-256 Object Category Data Set," technical report, California Inst. of Tech nology, 2007.
- [38] D. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints,"
Int'l J. Computer Vision, vol. 60, pp. 91-110, 2004.- [39] F. Perronnin, Y. Liu, J. Sánchez, and H. Poirier, "Large-Scale Image Retrieval with Compressed Fisher Vectors,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 3384-3391, 2010.- [40] D. Nistér and H. Stewénius, "Scalable Recognition with a Vocabulary Tree,"
Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2161-2168, 2006.- [41] H. Jégou and O. Chum, "Negative Evidences and Co-Occurrences in Image Retrieval: The Benefit of PCA and Whitening,"
Proc. 12th European Conf. Computer Vision (ECCV '12), pp. 774-787, 2012. |