ABSTRACT
In this paper, we propose a novel method to learn similarity-preserving hash functions for approximate nearest neighbor (NN) search. The key idea is to learn hash functions by maximizing the alignment between the similarity orders computed from the original space and the ones in the hamming space. The problem of mapping the NN points into different hash codes is taken as a classification problem in which the points are categorized into several groups according to the hamming distances to the query. The hash functions are optimized from the classifiers pooled over the training points. Experimental results demonstrate the superiority of our approach over existing state-of-the-art hashing techniques.
- L. Cao, Z. Li, Y. Mu, and S.-F. Chang. Submodular video hashing: a unified framework towards video pooling and indexing. In ACM Multimedia, pages 299--308, 2012. Google ScholarDigital Library
- L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In CVPR 2004 Workshop on Generative-Model Based Vision, 2004. Google ScholarDigital Library
- Y. Gong and S. Lazebnik. Iterative quantization: A procrustean approach to learning binary codes. In CVPR, pages 817--824, 2011. Google ScholarDigital Library
- J. He, S. Chang, R. Radhakrishnan, and C. Bauer. Compact hashing with joint optimization of search accuracy and time. In CVPR, pages 753--760, 2011. Google ScholarDigital Library
- J. He, J. Feng, X. Liu, T. Cheng, T.-H. Lin, H. Chung, and S.-F. Chang. Mobile product search with bag of hash bits and boundary reranking. In CVPR, pages 3005--3012, 2012. Google ScholarDigital Library
- J. He, W. Liu, and S. Chang. Scalable similarity search with optimized kernel hashing. In KDD, pages 1129--1138, 2010. Google ScholarDigital Library
- J. Heo, Y. Lee, J. He, S. Chang, and S. Yoon. Spherical hashing. In CVPR, pages 2957--2964, 2012. Google ScholarDigital Library
- P. Indyk and R. Motwani. Approximate nearest neighbors: Towards removing the curse of dimensionality. In STOC, pages 604--613, 1998. Google ScholarDigital Library
- H. Jégou, L. Amsaleg, C. Schmid, and P. Gros. Query-adaptative locality sensitive hashing. In ICASSP. IEEE, apr 2008.Google ScholarCross Ref
- H. Jégou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell., 33(1):117--128, 2011. Google ScholarDigital Library
- J. Ji, J. Li, S. Yan, B. Zhang, and Q. Tian. Super-bit locality-sensitive hashing. In NIPS, pages 108--116, 2012.Google ScholarDigital Library
- Y.-G. Jiang, J. Wang, X. Xue, and S.-F. Chang. Query-adaptive image search with hash codes. IEEE Transactions on Multimedia, 15(2):442--453, 2013.Google ScholarDigital Library
- S. Kim, Y. Kang, and S. Choi. Sequential spectral learning to hash with multiple representations. In ECCV (5), pages 538--551, 2012. Google ScholarDigital Library
- W. Kong and W.-J. Li. Isotropic hashing. In NIPS, pages 1655--1663, 2012.Google ScholarDigital Library
- W. Kong, W.-J. Li, and M. Guo. Manhattan hashing for large-scale image retrieval. In SIGIR, pages 45--54, 2012. Google ScholarDigital Library
- B. Kulis and T. Darrell. Learning to hash with binary reconstructive embeddings. In NIPS, pages 1042--1050, 2009.Google ScholarDigital Library
- B. Kulis and K. Grauman. Kernelized locality-sensitive hashing. IEEE Trans. Pattern Anal. Mach. Intell., 34(6):1092--1104, 2012. Google ScholarDigital Library
- Y.-H. Kuo, K.-T. Chen, C.-H. Chiang, and W. H. Hsu. Query expansion for hash-based image object retrieval. In ACM Multimedia, pages 65--74, 2009. Google ScholarDigital Library
- Z. Li, H. Ning, L. Cao, T. Zhang, Y. Gong, and T. Huang. Learning to search efficiently in high dimensions. In NIPS, pages 1710--1718, 2011.Google Scholar
- R. Lin, D. Ross, and J. Yagnik. Spec hashing: Similarity preserving algorithm for entropy-based coding. In CVPR, pages 848--854, 2010.Google ScholarCross Ref
- W. Liu, J. Wang, R. Ji, Y. Jiang, and S. Chang. Supervised hashing with kernels. In CVPR, pages 2074--2081, 2012. Google ScholarDigital Library
- W. Liu, J. Wang, S. Kumar, and S. Chang. Hashing with graphs. In ICML, pages 1--8, 2011.Google ScholarDigital Library
- X. Liu, J. He, D. Liu, and B. Lang. Compact kernel hashing with multiple features. In ACM Multimedia, pages 881--884, 2012. Google ScholarDigital Library
- Y. Mu, J. Shen, and S. Yan. Weakly-supervised hashing in kernel space. In CVPR, pages 3344--3351, 2010.Google ScholarCross Ref
- Y. Mu and S. Yan. Non-metric locality-sensitive hashing. In AAAI, 2010.Google ScholarDigital Library
- J. Nocedal and S. Wright. Numerical Optimization, volume 104 of Springer Serials in Operations Research. Springer-Verlag, London, 2006.Google Scholar
- M. Norouzi and D. Fleet. Minimal loss hashing for compact binary codes. In ICML, pages 353--360, 2011.Google ScholarDigital Library
- M. Norouzi, D. J. Fleet, and R. Salakhutdinov. Hamming distance metric learning. In NIPS, pages 1070--1078, 2012.Google ScholarDigital Library
- M. Raginsky and S. Lazebnik. Locality-sensitive binary codes from shift-invariant kernels. In NIPS, pages 1509--1517, 2009.Google ScholarDigital Library
- B. Russell, A. Torralba, K. Murphy, and W. Freeman. Labelme: A database and web-based tool for image annotation. International Journal of Computer Vision, 77(1--3):157--173, 2008. Google ScholarDigital Library
- R. Salakhutdinov and G. Hinton. Semantic hashing. Int. J. Approx. Reasoning, 50(7):969--978, 2009. Google ScholarDigital Library
- G. Shakhnarovich, T. Darrell, and P. Indyk. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. The MIT press, 2006. Google ScholarDigital Library
- J. Song, Y. Yang, Z. Huang, H. Shen, and R. Hong. Multiple feature hashing for real-time large scale near-duplicate video retrieval. In ACM Multimedia, pages 423--432, 2011. Google ScholarDigital Library
- C. Strecha, A. Bronstein, M. Bronstein, and P. Fua. Ldahash: Improved matching with smaller descriptors. IEEE Trans. Pattern Anal. Mach. Intell., 34(1):66--78, 2012. Google ScholarDigital Library
- A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. In CVPR, 2008.Google ScholarCross Ref
- K.-Y. Tseng, Y.-L. Lin, Y.-H. Chen, and W. H. Hsu. Sketch-based image retrieval on mobile devices using compact hash bits. In ACM Multimedia, pages 913--916, 2012. Google ScholarDigital Library
- J. Wang, O. Kumar, and S. Chang. Semi-supervised hashing for scalable image retrieval. In CVPR, pages 3424--3431, 2010.Google ScholarCross Ref
- J. Wang, S. Kumar, and S. Chang. Sequential projection learning for hashing with compact codes. In ICML, pages 1127--1134, 2010.Google ScholarDigital Library
- Y. Weiss, R. Fergus, and A. Torralba. Multidimensional spectral hashing. In ECCV (5), pages 340--353, 2012. Google ScholarDigital Library
- Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, pages 1753--1760, 2008.Google ScholarDigital Library
- H. Xu, J. Wang, Z. Li, G. Zeng, S. Li, and N. Yu. Complementary hashing for approximate nearest neighbor search. In ICCV, pages 1631--1638, 2011. Google ScholarDigital Library
- J. Yuan, G. Gravier, S. Campion, X. Liu, and H. Jégou. Efficient mining of repetitions in large-scale tv streams with product quantization hashing. In ECCV Workshops (1), pages 271--280, 2012. Google ScholarDigital Library
- Y. Zhuang, Y. Liu, F. Wu, Y. Zhang, and J. Shao. Hypergraph spectral hashing for similarity search of social image. In ACM Multimedia, pages 1457--1460, 2011. Google ScholarDigital Library
Index Terms
- Order preserving hashing for approximate nearest neighbor search
Recommendations
A Survey on Deep Hashing Methods
Nearest neighbor search aims at obtaining the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most widely used ...
Query-aware locality-sensitive hashing for approximate nearest neighbor search
Locality-Sensitive Hashing (LSH) and its variants are the well-known indexing schemes for the c-Approximate Nearest Neighbor (c-ANN) search problem in high-dimensional Euclidean space. Traditionally, LSH functions are constructed in a query-oblivious ...
Efficient approximate nearest neighbor search with integrated binary codes
MM '11: Proceedings of the 19th ACM international conference on MultimediaNearest neighbor search in Euclidean space is a fundamental problem in multimedia retrieval. The difficulty of exact nearest neighbor search has led to approximate solutions that sacrifice precision for efficiency. Among such solutions, approaches that ...
Comments