In content-based image retrieval (CBIR), color and texture are the most intuitive image features and it is widely used. But the current color feature can describe the semantics of the whole image effectively, but does not reflect characteristics of the color salience objects in an image. For the purpose of giving paper proposes a new color feature description model is proposed at first. This model integrates the intensity, the color contrast and self-saliency, sparsity and centricity saliency to describe human color visual perception of the image. Then, the new color feature descriptor is calculated by weighting the significant bit-plane histograms with color perception map. Finally, similarity measure is presented for the new color feature. Then for more efficient retrieval again the retrieved images are then compared for texture .Now because of the texture of salience retrieved image more accurate images can be retrieved. Experiment results show that the proposed color and texture feature is more accurate and efficient in retrieving images with user-interested color objects. Here a typical query can be a region of interest provided by the user, such as outlining patch in satellite image. Compared with the other retrieval methods, the proposed technique improves the retrieval accuracy effectively.