Content-based image retrieval for big visual data using image quality assessment model

摘要

Modern technology made storing, sharing, organizing huge amounts of data simple through the Internet of things. Search engines and query-based retrieval databases made access to relevant data easy through ranking and indexing based on content stored. This paper presents a content-based image retrieval technique using image quality assessment (IQA) model. The scheme combines two images IQA model namely mean-structural-similarity-index measure (MSSIM) and feature-similarity-index measure (FSIM) to take the relative advantage of each other. The MSSIM algorithm believes that human visual system can greatly reorganize the structural information from an image signal. On the other hand, color information is extracted using FSIM model. A combination of four image features i.e. luminance, contrast, structure, and color are used for similarity matching. Extensive experiments are carried out and assessment results reveal the outperforming result of the proposed technique with other related scheme.