An Approach for Image Search and Retrieval by Cluster-Based Indexing of Binary MKSIFT Codes

Abstract

Image search is an information retrieval approach that gained remarkable attention in the areas like multimedia and computer vision. The first work presented a cross-indexing-based approach for image retrieval using multiple kernel scale-invariant feature transform (MKSIFT), where the key point descriptor was calculated using two kernel functions. Previous work had complexity issues while dealing with the large databases, and hence, to avoid this, cluster-based indexing of binary MKSIFT codes is presented. The proposed cluster-based indexing scheme uses the MKSIFT feature extraction and the Particle Swarm-Fractional Bacterial foraging optimization algorithm for extracting the useful features from the images. Also, the Bayesian fuzzy clustering scheme is employed for grouping the images in the database into several clusters. The search index is constructed for the user query, and Bhattacharya distance between the cluster centroids and the search index is calculated to identify the optimal cluster. Then, finally, the images present in the optimal centroid are retrieved. The experimentation of the proposed cluster-based indexing scheme is analysed for the various query from the users. From the analysis, it is evident that the proposed cluster-based indexing scheme has achieved improved performance with the mean values of 0.9656, 0.9489 and 0.9049, for precision, recall and F measure, respectively.