Cited By
View all- Xu YLiu XWang BTao RXia KCao X(2021)Fast Nearest Subspace Search via Random Angular HashingIEEE Transactions on Multimedia10.1109/TMM.2020.297745923(342-352)Online publication date: 2021
Locality Preserving Hashing Method for Image Retrieval
Authors: 
Qiang Li, Yahong Han, Jianwu DangAuthors Info & Claims
Qiang Li, Yahong Han, Jianwu DangAuthors Info & ClaimsPages 148 - 151
Abstract
Binary hashing is widely used in efficient similarity computing for image/video search. In this work, we propose a Locality Preserving Hashing (LPH) method to improve the image retrieval performance, we first project high-dimensional feature space into low dimension using locality preserving method, in which the locality properties can be well maintained, then binaries the low-dimensional feature into binary index code using a heuristic projection strategy; in retrieval stage, we compute the Hamming distance of the cell indices by implementing XOR and bit-count operations, obtain the similarity result and finally get the target images. We evaluate the algorithm on four public image datasets and compare it with some popular methods, and the experimental results show the validity of our proposed method.
References
[1]
W. M. Smeulders, M. Worring, S. Santini, A. Gupta and R. Jain. Content-Based Image Retrieval at the End of the Early Years. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(12), 1349--1380, 2000.
[2]
Y. Han, F. Wu, D. Tao, J. Shao, Y. Zhuang and J. Jiang. Sparse Unsupervised Dimensionality Reduction for Multiple View Data. IEEE Trans. on Circuits and Systems for Video Technology, 22(10), 1485--1496, 2012.
[3]
L. Pauleve, H. Jegou and L. Amsaleg. Locality sensitive hashing: a comparison of hash function types and querying mechanisms. Pattern Recogn. Lett., 31(11), 1348--1358, 2010.
[4]
Y. Mu, J. Shen and S. Yan. Weakly-supervised hashing in kernel space. In CVPR 2010, pages. 3344--3351.
[5]
W. Liu, J. Wang, R. Ji, Y. and Shih-Fu Chang. Supervised Hashing with Kernels. CVPR 2012, pages. 2074--2081.
[6]
Y. Gong and S. Lazebnik. Iterative quantization: A Procrustean approach to learning binary codes. In CVPR, pages 817-824, 2011.
[7]
A. Gordo and F. Perronnin. Asymmetric distances for binary embeddings. In CVPR, pages. 729-736, 2011.
[8]
Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, pages. 1753-1760, 2008.
[9]
J. Wang, S. Kumar, and S. Chang. Semi-supervised hashing for scalable image retrieval. In CVPR, pages. 3424 -3431, 2010.
[10]
K. He, F. Wen and J. Sun. K-means Hashing - an Affinity-Preserving Quantization Method for Learning Binary Compact Codes. In CVPR, pages. 2938-2945, 2013
[11]
X. He and P. Niyogi. Locality Preserving Projections. In NIPS, pages. 153-160, 2003.
[12]
T. Fawcett. An introduction to ROC analysis. Pattern Recogn. Lett., 27(8), 861--874, 2006.
[13]
Y. Yang and J. Pedersen. A comparative study on feature selection in text categorization. In ICML, pages. 412-420, 1997.
Index Terms
- Locality Preserving Hashing Method for Image Retrieval
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Information & Contributors
Information
Published In
July 2014
430 pages
ISBN:9781450328104
DOI:10.1145/2632856
- General Chairs:
- Hanzi Wang,
- Larry Davis,
- Program Chairs:
- Wenwu Zhu,
- Stephan Kopf,
- Yanyun Qu,
- Publications Chairs:
- Jun Yu,
- Jitao Sang,
- Tao Mei
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In-Cooperation
- NSF of China: National Natural Science Foundation of China
- Beijing ACM SIGMM Chapter
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 July 2014
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- Research
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ICIMCS '14
ICIMCS '14: International Conference on Internet Multimedia Computing and Service
July 10 - 12, 2014
Xiamen, China
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Overall Acceptance Rate 163 of 456 submissions, 36%
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Cited By
View all- Xu YLiu XWang BTao RXia KCao X(2021)Fast Nearest Subspace Search via Random Angular HashingIEEE Transactions on Multimedia10.1109/TMM.2020.297745923(342-352)Online publication date: 2021
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