Automatic retrieval of similar patterns in image database using texture and color information

Content-based indexing and retrieval of image database has become quite a popular research focus during the past few years. Although several retrieval approaches based on low-level features have been proposed up to now, an efficient combination of these features which would remarkably improve the performance still needs to be developed. In the paper oriented towards the retrieval of images in a textured color image database, we propose a novel approach which effectively combines both the texture and the color information in a non-separate way. In the approach, we apply adaptive wavelet frame packet analysis which we proposed earlier to both the transformed texture channel and the color channel, we obtain textured features, colored features and correlated features of both texture and color from all the decomposed subbands and we measure similarity of images (features) using a simple and symmetric distance. Images are returned in order of similarity to the query sample. Experiments show that the proposed approach retrieves images in a progressive way. It can produce appealing performance in terms of both retrieval efficiency and retrieval effectiveness.