Improving content-based image retrieval with query-candidate relationship

This paper proposes a query-candidate relationship method to improve content-based image retrieval (CBIR). In our approach, each image is first transformed into the YUV color space. Then, the histogram for each component (i.e., luminance Y, blue chrominance U, and red chrominance V) of the image is obtained, which is served as the color feature of the image. To compensate the inherent shortcoming of the color histograms, i.e., without considering the spatial information (such as object location, shape, and texture), the discrete cosine transform (DCT) is applied to extract the spatial features from the Y component of images. In addition, a query-candidate relationship method is further introduced to analyze the mutual similarity between the query image and the candidate images, so as to improve the retrieval. Experimental results show the effectiveness of our approach.