随着图象数据的大量涌现，基于内容的图象检索技术已成为图象数据库领域的研究热点，在图象检索系统中，由于颜色直方图方法简单方便，所以它已成为CBIR系统中最常用的一种技术方法，然而，经典的颜色直方图方法存在诸多缺陷，例如它不能表示图象中的空间分布信息。为此，人们提出了直方图细化技术，即将图象的颜色分布表示扩充成为颜色和其他相关特征的联合分布。为了进一步提高图象检索能力，在分析图象特征的基础上，给出了两种加权直方图模型；其一是将图象的颜色分布和细节信号能量的分布集成到单个直方图之中；另一种模型是将图象颜色及其边界强度的联合分布集成到一个直方图中。这两种方法不仅保持了经典直方图简单方便的特点；同时又有效地将空间信息集成到直方图中，实验结果表明，这些加权直方图表示均具有较强的图象辨别能力。

As very large collections of images are becoming common, there is a growing interest in image database that can be queried based on image content. Content based image retrieval(CBIR) has become an important research issue for image database. As color histogram is simple to compute yet effective as a feature in detecting image to image similarity, it is an image feature widely used in CBIR. However, using the classical color histogram for indexing has a number of drawbacks. E.g. it does not contain information about the spatial locations or distributions of pixels in an image. To overcome its limitations, the refined histograms techniques are proposed on the basis of joint distribution of color and other features. In this paper, in order to provide a more accurate description of the image content, we propose two histogram models to refine the description of each pixel by some local features. One model is presented to integrate both color distribution and detail signal energy into a single histogram. Another is presented to integrate the edge strength into the definition of the color histogram. The experimental results show our histogram approaches can achieve an increased discriminative power compared to the classical color histogram technique for image retrieval.