基于SIFT和PCA的图像感知哈希方法
A perceptual image hashing,method via SIFT and PCA
孙锐 1闫晓星 2高隽3
作者信息
- 1. 合肥工业大学计算机与信息学院,安徽合肥230009;奇瑞汽车博士后工作站,安徽芜湖241009
- 2. 合肥工业大学光电技术研究院,安徽合肥230009
- 3. 合肥工业大学计算机与信息学院,安徽合肥230009
- 折叠
摘要
提出了一种新颖的基于尺度不变特征变换(SIFT)和主成分分析(PCA)的感知哈希方法.SIFT特征在通常的图像处理中具有很强的稳定性,并具有尺度和旋转不变性,通过对哈希生成两阶段框架的详细分析,SIFT算法用来提取图像的局部特征点,PCA用来对特征数据的信息压缩.每个特征点的PCA基的叠加构成图像哈希,在叠加中采用了伪随机处理,增强了算法安全性,图像之间的相似度通过哈希的归一化相关值来确定.实验分析表明该方法对各种复杂攻击,如图像旋转、光照变化、图像滤波等具有较好的稳健性,对比基于非负矩阵分解的图像哈希方法在图像识别应用中具有更好的性能.
Abstract
This paper presents a novel perceptual hashing method based on scale invariant feature transform (SIFT) and principal component analysis (PCA).SIFT features are invariant to image scaling and rotation,and stable to common image processing.Through the detailed analysis of two-stage framework of generating hash,SIFT is used to capture the local features of image.PCA is used to compress features information.Final hash is generated by summing PCA basis of each key point.The method uses pesduo-randomly processing for enhancing security of the algorithm.The similarity of images is determined by hash normalization correlation.Test results indicate that the proposed method is robust to various types of attacks such as image rotation,illumination change and filtering,etc.It is superior in image identification compared with the image hashing using non-negative matrix factorization.
关键词
尺度不变特征变换/主成分分析/感知哈希/图像识别Key words
SIFT/PCA/perceptual hashing/image identification引用本文复制引用
出版年
2013
NETL
NSTL
维普
万方数据