Dominant Feature Extraction in Block-DCT Domain

Automatically retrieving images through their low-level visual features has become one of the challenging areas of research recently. Among those distinguishing features, the texture features are one of the main themes in content-based image retrieval (CBIR). In this paper, we propose a novel technique to extract dominant features of images in block-DCT domain. The image is first converted to YUV color space and divided into four subblocks. The Y-component in each subblock is then transformed into DCT coefficients, some regions of which characterize different directional texture feature of that subblock. The directional textures in all subblocks are concatenated together as a single feature vector and used for indexing and retrieval of images. The experimental results show that using proper size of block-DCT to emphasize the regional properties of an image while maintaining its global view performs well in CBIR.