一种基于区域的图像检索方法的研究

Research on a region-based image retrieval approach

针对目前基于全局特征的图像检索系统存在的局限性,提出了一种基于区域的检索方案。首先应用K均值聚类算法将图像中的像素按颜色进行聚类,每一类近似对应于图像中的一个一致性区域,在区域上提取颜色和纹理特征。这种方式将检索过程深入到图像内部的物体中去,在一定程度上体现了图像的语义特性;在相似性匹配阶段,提出了一种基于区域的相似性匹配算法,并在实验中证明了其有效性。

A region-based image retrieval approach is proposed in this paper, aiming at the shortcomings of current global-feature-based retrieval systems. Pixels of an image are clustered into groups using K means algorithm, while one group corresponds to a coherent region in the image. Color and texture features are extracted from inside of the regions for similarity measure. This concept makes the retrieval process go to the image content, and thus, it embodies the semantic information of images. For the similarity matching stage, a region-based algorithm is pmpesed, whose effectiveness is verified in the experiments.