北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (2): 36-41.doi: 10.13190/j.jbupt.2018-116

• 论文 • 上一篇    下一篇

基于主成分分析的图像哈希算法

赵珊, 李永思   

  1. 河南理工大学 计算机科学与技术学院, 河南 焦作 454003
  • 收稿日期:2018-06-20 出版日期:2019-04-28 发布日期:2019-04-09
  • 通讯作者: 李永思(1994-),女,硕士生,E-mail:yongsili888@126.com. E-mail:yongsili888@126.com
  • 作者简介:赵珊(1975-),女,副教授.
  • 基金资助:
    河南省高等学校重点科研项目(18B520017);河南理工大学博士基金项目(B2014-043)

Imaging Hashing Based on Principal Component Analysis

ZHAO Shan, LI Yong-si   

  1. College of Computer Science and Technology, Henan Polytechnic University, Henan Jiaozuo 454003, China
  • Received:2018-06-20 Online:2019-04-28 Published:2019-04-09

摘要: 提出了一种基于主成分分析的图像哈希算法.采用主成分分析对样本进行降维,取位于变换矩阵顶端最具有识别信息的少量特征向量构造投影矩阵,再对降维后样本进行局部保持映射,同时,对主成分分析投影矩阵进行随机旋转,形成多个小投影矩阵,采用矩阵拼接方法将小投影矩阵合并构造编码投影矩阵;最后,将训练样本投影到编码投影矩阵,得到降维样本,并对其进行哈希编码,得到最终的二进制编码.实验结果证明,同其他经典算法相比,该算法具有较好的稳定性,可降低内存消耗,并提高效率.

关键词: 哈希, 主成分分析, 局部保持投影, 图像处理

Abstract: A novel image Hashing based on principal component analysis (PCA) was proposed. PCA was introduced to reduce dimension of samples, and the projection matrix was achieved by choosing several eigenvectors which have higher recognition ability. Based on which, the reduced-sample was mapped with locality preserving projection (LPP). Meanwhile, the projection matrix of principal component analysis was randomly rotated to form a series of transformational matrixes. The matrix stitching was adopted to construct the final code projection matrix. Finally, the original samples were projected into the code projection matrix to get a reduced dimensional sample, and the Hashing code was used to achieve the final binary encoding. Experiments show that the proposed method has better stability, lower memory consumption and higher efficiency compared with other traditional methods.

Key words: Hashing, principal component analysis, locality preserving projection, image processing

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