A Weighted Hashing Algorithm Based on Local Structured Information for Image Retrieval

doi:10.7652/xjtuxb201610012

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A Weighted Hashing Algorithm Based on Local Structured Information for Image Retrieval

更新时间：2025-07-09

- A Weighted Hashing Algorithm Based on Local Structured Information for Image Retrieval
- Vol. 50, Issue 10, Pages: 78-85(2016)
- 作者机构：
  
  空军工程大学航空航天工程学院,西安,710038
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201610012
  CLC： TP391
- Online First：10 October 2016，
  
  Published：2016
- 稿件说明：
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A Weighted Hashing Algorithm Based on Local Structured Information for Image Retrieval[J]. 2016, 50(10): 78-85.
DOI：

A Weighted Hashing Algorithm Based on Local Structured Information for Image Retrieval[J]. 2016, 50(10): 78-85. DOI： 10.7652/xjtuxb201610012.

摘要

针对图像检索领域中现有哈希算法仅考虑数据的全局信息

同时平等地对待每一维投影数据

导致得到的哈希码不能很好地保持原始数据的相似性的问题

提出利用图像数据局部结构信息的加权哈希(WLSH)算法。该算法同时考虑原始图像空间的局部结构信息和投影数据的各维方差

首先利用数据之间的相似性构建关系矩阵

进而获得原始图像数据的局部结构信息; 然后通过迭代量化的方法寻找最优的正交旋转矩阵

使得投影以后的量化误差最小; 最后通过权重矩阵平衡各维方差

保证每一维的单位编码信息相同

从而实现对原始数据最优的保距映射。在图像库上的实验结果表明

WLSH算法应用于图像检索时在查准率和查全率上比主成分分析-迭代量化方法分别提高了3%和2%。

Abstract

A weighted local structured Hashing(WLSH)method is proposed to address the problem in the field of image retrieval that most existing Hashing methods only consider the global information and treat each projected dimension equivalently

which leads to a problem that the binary codes cannot efficiently preserve the data similarity. The proposed method considers local structure information of original image data and the variance of each projected dimension simultaneously. An affinity weight matrix is built to describe the relationship between data points and to acquire local structure information of the original image data. Then

an iterative quantization is used to find an optimal orthogonal transformation matrix and to minimize the quantization error. Finally

a weighted matrix is used to balance the variances and to guarantee equivalent information of each Hashing bits

thus the data similarity is effectively preserve. Experimental results based on some large scale datasets show that the precision and recall of the WLSH algorithm are improved by 3% and 2% over principal component analysis-iterative quantization

respectively.

关键词

Keywords

references

李武军, 周志华. 大数据哈希学习: 现状与趋势 [J]. 科学通报, 2015, 60(5/6): 485-490.

LI Wujun, ZHOU Zhihua. Learning to Hash for big data: current status and future trends [J]. Chin Sci Bull, 2015, 60(5/6): 485-490.

GIONIS A, INDYK P, MOTWANI R. Similarity search in high dimensions via Hashing [C]∥International Conference on Very Large Data Bases. New York, USA: ACM, 2000: 518-529.

KULIS B, JAIN P, GRAUMAN K. Fast similarity search for learned metrics [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2143-2157.

赵永威, 李弼程, 彭天强, 等. 一种基于随机化视觉词典组和查询扩展的目标检索方法 [J]. 电子与信息学报, 2012, 34(5): 1154-1161.

ZHAO Yongwei, LI Bicheng, PENG Tianqiang, et al. An object retrieval method based on randomized visual dictionaries and query expansion [J]. Journal of Electronics Information Technology, 2012, 34(5): 1154-1161.

WEISS Y, TORRALBA A, FERGUS R. Spectral hashing [C]∥ Proceedings of the 22nd Annual Conference on Neural Information Processing Systems. Cambridge, MA, USA: MIT, 2008: 1753-1760.

马艳萍, 姬光荣, 邹海林, 等. 数据依赖的多索引哈希算法 [J]. 西安电子科技大学学报, 2015, 42(4): 159-164.

MA Yanping, JI Guangrong, ZOU Hailin, et al. Data-oriented multi-index Hashing [J]. Journal of Xidian University, 2015, 42(4): 159-164.

王秀美, 丁利杰, 高新波. 一种相似度保持的线性嵌入哈希方法 [J]. 西安电子科技大学学报, 2016, 43(1): 94-98.

WANG Xiumei, DING Lijie, GAO Xinbo. Linear embedding Hashing method in preserving similarity [J]. Journal of Xidian University, 2016, 43(1): 94-98.

刘贤焯, 王劲林, 朱明, 等. Hash表与B+树相结合的高效目录索引结构 [J]. 西安交通大学学报, 2013, 47(4): 105-111.

LIU Xianzhuo, WANG Jinlin, ZHU Ming, et al. Effective directory index framework taking advantages of Hash table and B+tree [J]. Journal of Xi'an Jiaotong University, 2013, 47(4): 105-111.

GONG Y C, LAZEBNIK S. Iterative quantization: a procrustean approach to learning binary codes for large scale image retrieval [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(12): 2916-2929.

LIN G S, SHEN C H, SHI Q F, et al. Fast supervised Hashing with decision trees for high-dimensional data [C]∥ Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE Computer Society, 2014: 1971-1978.

WANG Q F, SHEN B, WANG S M, et al. Binary codes embedding for fast image tagging with incomplete labels [C]∥ 13th European Conference on Computer Vision. Berlin, Germany: Springer-Verlag, 2014: 425-439.

WANG J, KUMAR S, CHANG S F. Semi-supervised Hashing for scalable image retrieval [C]∥ IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2010: 3424-3431.

XIA Y, HE K M, KOHLI P, et al. Sparse projections for high-dimensional binary codes [C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2015: 3332-3339.

LIU W, WANG J, KUMAR S, et al. Hashing with graphs [C]∥Proceedings of the 28th International Conference on Machine Learning. New York, USA: Association for Computing Machinery, 2011: 1-8.

SCHONEMANN P. A generalized solution of the orthogonal Procrustes problem [J]. Psychometrika, 1966, 31(1): 1-10.

LENG Cong, CHENG Jian, YUAN Ting, et al. Learning binary codes with Bagging PCA [C]∥Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Berlin, Germany: Springer Verlag, 2014: 177-192.

LIU X L, HE J F, DENG C, et al. Collaborative Hashing [C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE Computer Society, 2014: 2147-2154.

RAGINSKY M, LAZEBNIK S. Locality-sensitive binary codes from shift-invariant kernels [C]∥23rd Annual Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc., 2009: 1509-1517.

JEGOU H, DOUZE M, SCHMID C, et al. Aggregating local descriptors into a compact image representation [C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE Computer Society, 2010: 3304-3311.

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