A comprehensive assessment of content-based image retrieval using selected full reference image quality assessment algorithms

Abstract

In the area of full-reference (FR) objective 'image quality assessment' (IQA), a huge amount of improvement has been done in the last few years that can calculate image quality, consistently. Contrarily, query-based image/video databases and search engines retrieve related data using 'ranking and indexing' depending on stored content. It is also to be noted that both techniques use feature extraction to achieve their goal. The efficiency of seven selected FR-IQA schemes is described in this article for the retrieval of an image signal. Extensive tests are done on two freely accessible databases. The comparison results express that mean-structural-similarity-index-measure (MSSIM), and feature-similarity-index-measure (FSIM) offer superior outcome than other IQA models. Our assessment outcome and the related thought will be very much useful for the researchers to understand the latest application areas of the IQA model in the area of image searching and retrieval.

Analysis of combined approaches of CBIR systems by clustering at varying precision levels

“…In full image retrieval methods, the features are taken out without segmenting into the regions of the entire image. In region-based methods, earlier to the extraction of the features the image is segmented into separate regions [7]- [9]. Few of the existing CBIR methods: i) Blobworld, is based on a region-based image retrieval method, developed by Carson et al with computer vision group in UC Berkeley [10].…”