提出一种建立在HSV空间颜色分类和形态特征基础上的图像Hash方法，用于图像检索.将图像尺寸规格化，并根据HSV空间中各分量的取值范围，将像素归为11类，在各类颜色成分中提取亮度、大小、形态等特征，加权得到Hash值以构成图像库的索引表，从而实现基于内容的图像检索（contentbased image retrieval，CBIR）.与其他方法相比，用该方法提取的图像特征除颜色外还包含形态特征，能较好地体现图像内容.实验结果表明，该方法具有良好的性能.

This paper proposes a method of perceptual image hashing to be used in contentbased image retrieval (CBIR). The method is based on an analysis of color and shape/pattern features. The image is first scaled to a standard size, with the pixels classified into 11 categories according to the HSV parameters. From each category, features such as brightness, size and shape are extracted and used to construct the image hash. Since features extracted contain shape and pattern information apart from color, they can effectively represent the image contents. The features are properly weighted to form a hash used in CBIR. Experimental results confirm effectiveness of the proposed method.