A CBIR Technique Based on the Combination of Shape and Color Features

In CBIR techniques, image retrieval based on object-based features are more precise to retrieve appropriate relevant images. So, in this paper, a CBIR technique is proposed using extracted combined shape and color features from image object region. In this particular work, some significant statistical parameters are calculated from image object or shape region by gray-level co-occurrence matrix and simultaneously, color features are extracted from the color object using color autocorrelogram. Initially, RGB color images are transformed into YCbCr color space, and subsequently, the active contour is employed on Y-component to obtain the foreground and the background regions. Shape or object feature is located in the foreground region of Y-component and gray-level co-occurrence matrix provides some statistical parameters. We have also computed some statistical parameters from the background region to improve the image retrieval performance. Afterward, an intermediate color object image is reconstructed by combining foreground image region along with chrominance components for deriving the prominent color information. We have employed color autocorrelogram over this newly constructed intermediate image. Finally, all the computed features are combined together to form the ultimate feature vector. The proposed technique is tested over two benchmark databases, i.e., Corel-1K and GHIM-10K and we have achieved satisfactory results in object-based images.