At present, the accuracy of image retrieval is a difficult problem to study, the main reason is the method of feature extraction. In order to improve the precision of image retrieval, a new image retrieval method based on multi-feature called CAUC (Comprehensive Analysis based on the Underlying Characteristics) was presented. First, based on YUV color space, the mean value and the standard deviation were used to extract the global feature from an image that depicted the global characteristics of the image, and the image bitmap was introduced to describe the local characteristics of the image. Secondly, the compactness and Krawtchouk moment were extracted to describe the shape features. Then, the texture features were described by the improved four-pixel co-occurrence matrix. Finally, the similarity between images was computed based on multi-feature fusion, and the images with high similarity were returned.On Corel-1000 image set, the comparative experiments with method which only considered four-pixel co-occurrence matrix showed that the retrieval time of CAUC was greatly reduced without significantly reducing the precision and recall. In addition, compared with the other two kinds of retrieval methods based on multi-feature fusion, CAUC improved the precision and recall with high retrieval speed. The experimental results demonstrate that CAUC method is effective to extract the image feature, and improve retrieval efficiency.