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.