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Sensitivity based image filtering for multi-hashing in large scale image retrieval problems

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Abstract

Hashing is an effective method to retrieve similar images from a large scale database. However, a single hash table requires searching an exponentially increasing number of hash buckets with large Hamming distance for a better recall rate which is time consuming. The union of results from multiple hash tables (multi-hashing) yields a high recall but low precision rate with exact hash code matching. Methods using image filtering to reduce dissimilar images rely on Hamming distance or hash code difference between query and candidate images. However, they treat all hash buckets to be equally important which is generally not true. Different buckets may return different number of images and yield different importance to the hashing results. We propose two descriptors, bucket sensitivity measure and location sensitivity measure, to score both the hash bucket and the candidate images that it contains using a location-based sensitivity measure. A radial basis function neural network (RBFNN) is trained to filter dissimilar images based on the Hamming distance, hash code difference, and the two proposed descriptors. Since the Hamming distance and the hash code difference are readily computed by all hashing-based image retrieval methods, and both the RBFNN and the two proposed sensitivity-based descriptors are computed offline when hash tables become available, the proposed sensitivity based image filtering method is efficient for a large scale image retrieval. Experimental results using four large scale databases show that the proposed method improves precision at the expense of a small drop in the recall rate for both data-dependent and data-independent multi-hashing methods as well as multi-hashing combining both types.

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References

  1. Kulis B, Grauman K (2012) Kernelized locality-sensitive hashing. IEEE Trans Pattern Anal Mach Intell 34(6):1092–1104

    Article  Google Scholar 

  2. Wang J, Kumar S, Chung S-F (2012) Semi-supervised hashing for large-scale search. IEEE Trans Pattern Anal Mach Intell 34(12):2393–2406

    Article  Google Scholar 

  3. Strecha C, Bronstein AM, Bronstein MM, Fua P (2012) LDAHash: improved matching with smaller descriptors. IEEE Trans Pattern Anal Mach Intell 34(1):66–78

    Article  Google Scholar 

  4. Chechik G, Sharma V, Shalit U, Bengio S (2010) Large scale online learning of image similarity through ranking. J Mach Learn Res 11:1109–1135

    MATH  MathSciNet  Google Scholar 

  5. Silpa-Anan C, Hartley R (2008) Optimised KD-tree for fast image descriptor matching. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 1–8

  6. Weber R, Schek HJ, Blott S (1998) A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: Proceedings of the 24th international conference on very large data bases, pp 194–205

  7. Satuluri V, Parthasarathy S (2012) Bayesian locality sensitive hashing for fast similarity search. In: Proceedings of the very large database endowment, pp 430–441

  8. Yeung DS, Ng WWY, Wang D, Tsang ECC, Wang X-Z (2007) Localized generalization error and its application to architecture selection for radial basis function neural network. IEEE Trans Neural Netw 18(5):1294–1305

    Article  Google Scholar 

  9. Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the 30th ACM symposium on theory of computing, pp 604–613

  10. Charikar MS (2002) Similarity estimation techniques from rounding algorithms. In: Proceedings of ACM symposium on the theory of computing, pp 380–388

  11. Bian W, Tao D (2010) Biased discriminant euclidean embedding for content-based image retrieval. IEEE Transa Image Process 19(2):545–554

    Article  MathSciNet  Google Scholar 

  12. Gorisse D, Cord M, Precioso F (2012) Locality-sensitive hashing for Chi2 distance. IEEE Trans Pattern Anal Mach Intell 34(2):402–409

    Article  Google Scholar 

  13. Kulis B, Jain P, Grauman K (2012) Fast similarity search for learned metrics. IEEE Trans Pattern Anal Mach Intell 31(12):2143–2157

    Article  Google Scholar 

  14. Gong Y, Lazebnik S, Gordo A, Perronnin F (2013) Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans Pattern Anal Mach Intell 35(12):2916–2929

    Article  Google Scholar 

  15. Ng WWY, Lv Y, Yeung DS, Chan PPK (2015) Two-phase mapping hashing. Neurocomputing 151:1423–1429

    Article  Google Scholar 

  16. Lv Y, Ng WWY, Zeng Z, Yeung DS, Chan PPK (2015) Asymmetric cyclical hashing for large scale image retrieval. IEEE Trans Multimed 17(8):1225–1235

    Article  Google Scholar 

  17. Matsushita Y, Wada T (2009) Principal component hashing: an accelerated approximate nearest neighbor search. Adv Image Video Technol LNCS 5414:374–385

    Article  Google Scholar 

  18. Wu C, Zhu J, Cai D, Chen C, Bu J (2013) Semi-supervised nonlinear hashing using bootstrap sequential projection learning. IEEE Trans Knowl Data Eng 25(6):1380–1393

    Article  Google Scholar 

  19. Shao J, Wu F, Ouyang C, Zhang X (2012) Sparse spectral hashing. Pattern Recognit Lett 33(3):271–277

    Article  Google Scholar 

  20. Li P, Wang M, Cheng J, Xu C, Lu H (2013) Spectral hashing with semantically consistent graph for image indexing. IEEE Trans Multimed 15(1):141–152

    Article  Google Scholar 

  21. Weiss Y, Torralba AB, Fergus R (2008) Spectral hashing. In: Proceedings conference on advances in neural information processing systems, pp 1753–1760

  22. Norouzi M, Fleet DJ (2011) Minimal loss hashing for compact binary codes. In: Proceedings of international conference on machine learning, pp 353–360

  23. Kulis B, Darrell T (2009) Learning to hash with binary reconstructive embeddings. Adv Neural Inf Process Syst 22:1042–1050

    Google Scholar 

  24. Salakhutdinov R, Hinton GE (2009) Semantic hashing. Int J Approx Reason 50(7):969–978

    Article  Google Scholar 

  25. Xu H, Wang J, Li Z, Zeng G, Li S, Yu N (2011) Complementary hashing for approximate nearest neighbor search. In: Proceedings of IEEE international conference on computer vision, pp 1631–1638

  26. Li P, Cheng J, Lu H (2013) Hashing with dual complementary projection learning for fast image retrieval. Neurocomputing 120:83–89

    Article  Google Scholar 

  27. Wang X, Qiu S, Liu K, Tang X (2014) Web image re-ranking using query-specific semantic signatures. IEEE Trans Pattern Anal Mach Intell 36:810–823

    Article  Google Scholar 

  28. Jiang Y-G, Wang J, Xue X, Chang S-F (2013) Query-adaptive image search with hash codes. IEEE Trans Multimed 15(2):442–453

    Article  Google Scholar 

  29. Norouzi M, Punjani A, Fleet DJ (2012) Fast search in Hamming space with multi-index hashing. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 3108–3115

  30. Ng WWY, Yeung DS, Firth M, Tsang ECC, Wang X-Z (2008) Feature selection using localized generalization error for supervised classification problems using RBFNN. Pattern Recognit 41(12):3706–3719

    Article  MATH  Google Scholar 

  31. LeCun Y, Bottou L, Bengio, Y, Haffner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp 2278–2324

  32. Krizhevsky A (2009) Learning multiple layers of features from tiny images. Master Thesis, University of Toronto, Toronto. http://www.cs.toronto.edu/ kriz/cifar.html

  33. Chua T-S, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) NUS-WIDE: a real-world web image database from National University of Singapore. In: Proceedings of the ACM international conference on image and video retrieval (Article no. 48)

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Acknowledgments

This work is supported by National Natural Science Foundation of China (61272201), a Program for New Century Excellent Talents in University (NCET-11-0162) of China, and the Fundamental Research Funds for the Central Universities (2015ZZ023). We would like to thank Dr. Jun Wang for providing the source code of SPLH and databases.

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Correspondence to Jinchen Li.

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Ng, W.W.Y., Li, J., Feng, S. et al. Sensitivity based image filtering for multi-hashing in large scale image retrieval problems. Int. J. Mach. Learn. & Cyber. 6, 777–794 (2015). https://doi.org/10.1007/s13042-015-0402-1

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  • DOI: https://doi.org/10.1007/s13042-015-0402-1

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