形状是图象中目标的重要特征,基于形状的图象检索近来在基于内容的图象库系统管理和应用中得到越来越多的重视.现已研制的系统存在两个问题,一是性能的不稳定性;二是相对于平移、旋转和尺度变换的变化性.针对以上问题,该文提出了一种新的基于形状的图象检索算法.此算法先对亮度图象进行小波模极大值变换以得到多尺度的边界图象,再利用7个不变矩提取每一尺度边界图象的特征,所有尺度上的矩共同组成图象的特征向量.图象的相似度用图象特征向量的归一化加权欧氏距离表示.用服装图象数据库进行试验得到的结果表明该算法能较好地描述

Shape is an important feature of objects in image and shape-based image retrieval has obtained more and more attentions in recent research on content-based management and utilization of image database system. Although several systems have been developed, two main shortcomings are still existed. The first is that the performance is not stable. The second is that the variance with respect to translation, scaling, and rotation. To cure the above problems, this paper presents a novel shape-based image retrieval algorithm. The algorithm first transforms the luminance image with wavelet modulus maximum to get multi-scale edge images, then employs a set of seven invariant moments to extract the features of image. Consequently, each image is characterized by a multi-scale moment vector in feature space. Similarity is given by the Euclidean distance between two images' normalized moment vectors. Experimental results on clothes image database show that this algorithm can well capture the shape and spatial information of image and it is invariant with respect to translation, scaling and rotation of objects. In addition, the algorithm is also tested with more complicated flower images in a database; the experimental results further verify the effectiveness of the algorithm.