基于PCA特征距离的图像哈希算法

Image Hashing Algorithm Based on PCA Feature Distance

本文将主成分分析(PCA)应用于图像哈希,设计基于特征距离的感知哈希算法。该算法从规范化图像中构造适合于数据降维的二次图像,接着对其进行PCA处理,用PCA降维特征的距离生成哈希序列。实验结果表明本文算法的接收机操作特性曲线的分类性能优于现有的3种哈希算法。大规模图像库的拷贝检测显示,本文算法有较好的拷贝检测性能。

Principal component analysis（PCA）is applied to image hashing algorithm and a perceptual hashing algorithm based on characteristic distance is proposed.In the proposed algorithm,a secondary image suitable for data dimension reduction is first constructed from the normalized image.Then,PCA is used to process secondary image,and the distance between PCA features is finally exploited to form image hash.Experimental results illustrate that classification performance of the proposed algorithm measured with receiver operating characteristics（ROC）curve is better than those of three existing hashing algorithms.Copy detection under a large-scale image database shows that the proposed algorithm has good performance in detecting image copies.