[1] Zuo Xin, Luo Limin, Shen Jifeng, et al. Learning compact binary codebased on multiple heterogeneous features [J]. Journal of Southeast University (English Edition), 2013, 29 (4): 372-378. [doi:10.3969/j.issn.1003-7985.2013.04.004]

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Learning compact binary codebased on multiple heterogeneous features()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

29

Issue:

2013 4

Page:

372-378

Research Field:

Computer Science and Engineering

Publishing date:

2013-12-20

Info

Title:

Learning compact binary codebased on multiple heterogeneous features

Author(s):

Zuo Xin¹;  2;  Luo Limin¹;  Shen Jifeng³;  Yu Hualong²

¹Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China
² School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
³School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Keywords:

hashing code;  linear discriminate analysis;  asymmetric boosting;  heterogeneous feature

PACS:

TP391.3

DOI:

10.3969/j.issn.1003-7985.2013.04.004

Abstract:

A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples in the feature space to boost the performance of each weak classifier in the asymmetric boosting framework. Then, the weak classifier based on a novel linear discriminate analysis(LDA)algorithm which is learned from the subspace of heterogeneous features is integrated into the framework. Finally, the proposed method deals with each bit of the code sequentially, which utilizes the samples misclassified in each round in order to learn compact and balanced code. The heterogeneous information from different modalities can be effectively complementary to each other, which leads to much higher performance. The experimental results based on the two public benchmarks demonstrate that this method is superior to many of the state-of-the-art methods. In conclusion, the performance of the retrieval system can be improved with the help of multiple heterogeneous features and the compact hash codes which can be learned by the imbalanced learning method.

References:

[1] Silpa-Anan C, Hartley R. Optimised KD-trees for fast image descriptor matching[C]//IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA, 2008: 1-8.
[2] Uhlmann J K. Satisfying general proximity/similarity queries with metric trees[J]. Information Processing Letters, 1991, 40(4):175-179.
[3] Gionis A, Indyk P, Motwani R. Similarity search in high dimensions via hashing[C]//Proceedings of the 25th International Conference on Very Large Data Bases. Edinburgh, Scotland, 1999: 518-529.
[4] Lv Q, Josephson W, Wang Z, et al. Multi-probe LSH: efficient indexing for high-dimensional similarity search[C]//Proceedings of the 33rd International Conference on Very Large Data Bases. Vienna, Austria, 2007: 950-961.
[5] Kulis B, Grauman K. Kernelized locality-sensitive hashing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(6): 1092-1104.
[6] Gong Y, Lazebnik S. Iterative quantization: a procrustean approach to learning binary codes[C]//IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA, 2011: 817-824.
[7] Weiss Y, Torralba A, Fergus R. Spectral hashing[C]//Advances in Neural Information Processing Systems. British Columbia, Canada, 2008:1-8.
[8] Lazebnik S, Raginsky M. Locality-sensitive binary codes from shift-invariant kernels [C]//Advances in Neural Information Processing Systems. British Columbia, Canada, 2009: 1509-1517.
[9] Salakhutdinov R, Hinton G. Semantic hashing[J]. International Journal of Approximate Reasoning, 2009, 50(7): 969-978.
[10] Kulis B, Darrell T. Learning to hash with binary reconstructive embeddings[C]//Advances in Neural Information Processing Systems. Vancouver, Canada, 2009:1042-1050.
[11] Norouzi M, Fleet D. Minimal loss hashing for compact binary codes[C]//Proceedings of the 28th International Conference on Machine Learning. Bellevue, WA, USA, 2011: 353-360.
[12] Wang J, Kumar S, Chang S F. Semi-supervised hashing for large-scale search[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(12):2393-2406.
[13] Liu W, Wang J, Kumar S, et al. Hashing with graphs[C]//Proceedings of the 28th International Conference on Machine Learning. Bellevue, WA, USA, 2011: 1-8.
[14] Strecha C, Bronstein, A M, Bronstein M M, et al. LDAHash: improved matching with smaller descriptors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(1):66-78.
[15] Andoni A, Indyk P. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions[C]//47th Annual IEEE Symposium on Foundations of Computer Science. Berkeley, CA, USA, 2006:459-468.
[16] Datar M, Immorlica N, Indyk P, et al. Locality-sensitive hashing scheme based on p-stable distributions[C]//Proceedings of the 20th Annual Symposium on Computational Geometry. New York, USA, 2004:253-262.
[17] Shakhnarovich G, Viola P, Darrell T. Fast pose estimation with parameter-sensitive hashing[C]//Proceedings of the 9th IEEE International Conference on Computer Vision. Nice, France, 2003: 750-757.
[18] Shen J F, Sun C Y, Yang W K, et al. A novel distribution-based feature for rapid object detection[J]. Neurocomputing, 2011, 74(17):2767-2779.
[19] Chang H, Ai H Z, Li Y, et al. High-performance rotation invariant multiview face detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(4): 671-686.

Memo

Memo:

Biographies: Zuo Xin(1980—), female, graduate; Luo Limin(corresponding author), male, doctor, professor, luo.list@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61305058), the Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.12KJB520003), the Natural Science Foundation of Jiangsu Province(No.BK20130471), the Scientific Research Foundation for Advanced Talents by Jiangsu University(No.13JDG093).
Citation: Zuo Xin, Luo Limin, Shen Jifeng, et al. Learning compact binary code based on multiple heterogeneous features.[J].Journal of Southeast University(English Edition), 2013, 29(4):372-378.[doi:10.3969/j.issn.1003-7985.2013.04.004]

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