Hybrid CBIR method using statistical, DWT‐Entropy and POPMV‐based feature sets

Abstract

Content-based image retrieval (CBIR) is an image retrieval technique that can retrieve images by matching its feature set values. This research focuses on a novel CBIR method namely hybrid CBIR method using statistical, Discrete Wavelet Transform (DWT)-Entropy and Peak-oriented Octal Pattern-derived Majority Voting (POPMV)-based feature sets (CBIR_SWPOPMV) to efficiently extract the relevant colour images from the colour image dataset. The doctrine of the proposed method is influenced by a novel texture descriptor namely POPMV which is an octal pattern based on the histogram peak information, to bring about a majority voting-based feature set and three histogram-based feature sets. Furthermore, to improve the retrieval accuracy, the DWT-based Entropy feature set and the statistical feature set are also included. Finally, the Euclidean distance-based matching process brings more favourable relevant images with respect to the query image. The proposed methodology is experimentally compared with the existing recent CBIR versions by using seven standard databases such as Corel-1k, USPTex, MIT-VisTex, KTH-TIPS, KTH-TIPS2a, KTH-TIPS2a, Colored Brodatz and a user-contributed database named DB_VEG.