基于小波分解的统计特征哈希

doi:10.3969/j.issn.0255-8297.2018.02.004

应用科学学报 ›› 2018, Vol. 36 ›› Issue (2): 247-254.doi: 10.3969/j.issn.0255-8297.2018.02.004

基于小波分解的统计特征哈希

沈麒, 赵琰

上海电力学院电子与信息工程学院, 上海 200090

收稿日期:2018-01-31 出版日期:2018-03-31 发布日期:2018-03-31
通信作者: 赵琰,副教授,研究方向:图像处理、多媒体技术,E-mail:yanzhao79@hotmail.com E-mail:yanzhao79@hotmail.com
基金资助:
上海市自然科学基金（No.15ZR1418500，No.15ZR1418400）资助

Statistical Feature Hashing Based on Wavelet Decomposition

SHEN Qi, ZHAO Yan

College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Received:2018-01-31 Online:2018-03-31 Published:2018-03-31

摘要/Abstract

摘要：

为了提高图像的拷贝检测效率和识别性能，提出一种基于小波分解的统计特征哈希.对图像预处理后通过三级小波分解提取图像的近似图像，再提取第3次小波分解所得近似图像的行与列的统计特征；将行与列统计特征的L2距离作为图像的不变特征，并将所有不变特征联合作为图像的最终哈希.实验结果表明，所提出的哈希算法的拷贝检测识别性能较好，效率较高.

关键词: 拷贝检测, 小波分解, 统计特征, L2距离

Abstract:

A statistical feature hash based on wavelet decomposition is proposed for the improvement of the image copy detection efciency and the recognition performance. In the proposal, an approximate image is frstly extracted from a preprocessed image by the threeorder wavelet decomposition. Secondly, the statistical features of the row and column of the approximate image of the third wavelet decomposition are extracted, and the L2 distance of the row and column statistical features is used as the invariant feature. All the invariant features are combined and used as the fnal hash of the image. Experimental results show that the proposed hash algorithm has better performance and higher efciency in copy detection.

Key words: statistical feature, wavelet decomposition, L2 distance, copy detection

中图分类号:

TP391

沈麒, 赵琰. 基于小波分解的统计特征哈希[J]. 应用科学学报, 2018, 36(2): 247-254.

SHEN Qi, ZHAO Yan. Statistical Feature Hashing Based on Wavelet Decomposition[J]. Journal of Applied Sciences, 2018, 36(2): 247-254.

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基于小波分解的统计特征哈希

Statistical Feature Hashing Based on Wavelet Decomposition

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