Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching

In this study, a novel technique for image retrieval based on selective regions matching using region codes is presented. All images in the database are uniformly divided into multiple regions and each region is assigned a 4-bit region code based upon its location relative to the central region. Dominant color and Local Binary Pattern (LBP) based texture features are extracted from these regions. Feature vectors together with their region codes are stored and indexed in the database. During retrieval, feature vectors of regions having region codes similar to the query image region are used for comparison. To reflect the user’s intent in query formulation in a better way, an effective technique for Region of Interest (ROI) overlapping block selection is also proposed. Region codes are further used to find relative locations of multiple ROIs in query and target images. The performance of the proposed approach is tested on the MPEG-7 CCD database and Corel image database. Experimental results show that the proposed approach increases the accuracy and reduces image retrieval time.