Abstract
Due to its high query speed and low storage cost, binary hashing has been widely used in approximate nearest neighbors (ANN) search. However, the binary bits are generally considered to be equal, which causes data points with different codes to share the same Hamming distance to the query sample. To solve the above distance measure ambiguity, bitwise weights methods were proposed. Unfortunately, in most of the existing methods, the bitwise weights and the binary codes are learnt separately in two stages, and their performances cannot be further improved. In this paper, to effectively address the above issues, we propose an adaptive mechanism that jointly generate the bitwise weights and the binary codes by preserving different types of similarity relationship. As a result, the binary codes are utilized to obtain the initial retrieval results, and they are further re-ranked by the weighted Hamming distance. This ANN search mechanism is termed AR-Rank in this paper. First, this joint mechanism allows the bitwise weights and the binary codes to be used as mutual feedback during the training stage, and they are well adapted to one other when the algorithm converges. Furthermore, the bitwise weights are required to preserve the relative similarity which is consistent with the nature of ANN search task. Thus, the data points can be accurately re-sorted based on the weighted Hamming distances. Evaluations on three datasets demonstrate that the proposed AR-Rank retrieval system outperforms nine state-of-the-art methods.
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References
Cheng Z, Shen J (2015) On very large scale test collection for landmark image search benchmarking. Signal Process 124:13–26
Cheng Z, Shen J (2016) On effective location-aware music recommendation. ACM Trans Inf Syst 34(2):1–32
Cheng Z, Shen J, Miao H (2016) The effects of multiple query evidences on social image retrieval. Multimedia Systems 22(4):509–523
Datar M, Immorlica N, Indyk P, Mirrokni VS (2004) Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the 20th annual symposium on computational geometry, pp 253–262
Fu H, Kong X, Wang Z (2016) Binary code reranking method with weighted hamming distance. Multimed Tools Appl 75(3):1391–1408
Gong Y, Lazebnik S (2011) Iterative quantization: a procrustean approach to learning binary codes. In: Proceedings of 2011 IEEE conference on computer vision and pattern recognition (CVPR), pp 817– 824
He K, Wen F, Sun J (2013) K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In: Proceedings of 2013 IEEE conference on computer vision and pattern recognition (CVPR), pp 2938–2945
Jegou H, Douze M, Schmid C (2011) Product quantization for nearest neighbor search. IEEE Trans Pattern Anal Mach Intell 33(1):117–28
Ji T, Liu X, Deng C, Huang L, Lang B (2014) Query-adaptive hash code ranking for fast nearest neighbor search. In: Proceedings of 2014 ACM international conference on multimedia, pp 1005–1008
Jiang Y-G, Wang J, Chang S-F (2011) Lost in binarization: query-adaptive ranking for similar image search with compact codes. In: Proceedings of 2011 ACM international conference on multimedia retrieval, pp 1–8
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
Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images, Computer Science Department, University of Toronto, Tech Rep
Liu W, Wang J, Kumar S, Chang SF (2011) Hashing with graphs. In: Proceedings of 2011 international conference on machine learning, pp 1–8
Liu W, Wang J, Mu Y-D, Kumar S, Chang S-F (2012) Compact hyperplane hashing with bilinear functions. In: Proceedings of 2012 international conference on machine learning (ICML)
Liu H, Ji R, Wu Y, Huang F (2016) Ordinal constrained binary code learning for nearest neighbor search. In: Proceedings of the 31st AAAI conference on artificial intelligence, pp 2238–2244
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91– 110
Norouzi M, Fleet DJ (2011) Minimal loss hashing for compact binary codes. In: Proceedings of 2011 international conference on machine learning, pp 353–360
Norouzi M, Blei DM, Salakhutdinov R (2012) Hamming distance metric learning. In: Proceedings of the advances in neural information processing systems, pp 1070–1078
Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–75
Terasawa K, Tanaka Y (2007) Spherical LSH for approximate nearest neighbor search on unit hypersphere. In: Proceedings of 2007 international conference on algorithms and data structures, pp 27– 38
Shen F-M, Zhou X, Yang Y, Song J-K, Shen H-T, Tao D-C (2016) A fast optimization method for general binary code learning. IEEE Trans Image Process 25(12):5610–5621
Shum HY, Zhang L, Zhang X (2012) Qsrank: Query-sensitive hash code ranking for efficient 𝜖-neighbor search. In: Proceedings of 2012 the IEEE conference on computer vision and pattern recognition (CVPR), pp 2058–2065
Silpaanan C, Hartley R (2008) Optimised KD-trees for fast image descriptor matching. In: Proceedings of 2018 IEEE conference on computer vision and pattern recognition (CVPR), pp 1–8
Song JK, Zhang HW, Li XP, Gao LL, Wang M, Hong RC (2018) Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Trans Image Process 27(7):3210–3221
Wang J, Kumar S, Chang SF (2012) Semi-supervised hashing for large-scale search. IEEE Trans Pattern Anal Mach Intell 34(12):2393–2406
Wang J, Liu W, Sun AX, Jiang YG (2013) Learning hash codes with listwise supervision. In: Proceedings of the IEEE international conference on computer vision (CVPR), pp 3032–3039
Wang J, Wang J, YU N, Li S (2013) Order preserving hashing for approximate nearest neighbor search. In: Proceedings of the 21st ACM international conference on multimedia, pp 133–142
Weiss Y, Torralba A, Fergus R (2008) Spectral hashing. In: Proceedings of 2008 the advances in neural information processing systems, pp 1753–1760
Xia Y, He K, Kohli P, Sun J (2015) Sparse projection for high-dimensional binary codes. In: Proceedings of 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 3332–3339
Xie L, Shen J, Han J, Zhu L, Shao L (2017) Dynamic multi-view hashing for online image retrieval. In: Proceedings of 2017 international joint conference on artificial intelligence, pp 3133–3139
Xu X, Shen F-M, Yang Y, Shen H-T, Li X-L (2017) Learning discriminative binary codes for large-scale cross-modal retrieval. IEEE Trans Image Process 26(5):2494–2507
Zhang J, Peng Y (2016) Query-adaptive image retrieval by deep weighted hashing. arXiv:1612.02541
Zhang L, Zhang Y, Tang J, Lu K, Tian Q (2013) Binary code ranking with weighted hamming distance. In: Proceedings of 2013 IEEE conference on computer vision and pattern recognition (CVPR), pp 1586–1593
Zhu L, Huang Z, Liu XB, He XN, Sun JD, Zhou XF (2017) Discrete multimodal hashing with canonical views for robust mobile landmark search. IEEE Trans Multimed 19(9):2066–2079
Zhu L, Huang Z, Chang XJ, Song JK, Shen HT (2017) Exploring consistent preferences: Discrete hashing with pair-exemplar for scalable landmark search. In: Proceedings of 2017 ACM on multimedia conference, pp 726–734
Zhu L, Huang Z, Chang X -J, Song J -K, Shen H -T (2017) Exploring consistent preferences: discrete hashing with pair-exemplar for scalable landmark search. In: Proceedings of the 2017 ACM on multimedia conference, pp 726–734
Zhu L, Huang Z, Li ZH, Xie L, Shen HT (2018) Exploring auxiliary context: discrete semantic transfer hashing for scalable image retrieval pp 1–13. IEEE Trans Neural Netw Learn Syst 29(11):5264– 5276
Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant No. 61841602), the Natural Science Foundation of Shandong Province of China (Grant No. ZR2018PF005) and the Doctoral Research Foundation of Shandong University of Technology (Grant No. 4041417009).
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Wang, Z., Zhang, LB., Sun, FZ. et al. Relative similarity preserving bitwise weights generated by an adaptive mechanism. Multimed Tools Appl 78, 24453–24472 (2019). https://doi.org/10.1007/s11042-018-6997-y
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DOI: https://doi.org/10.1007/s11042-018-6997-y