{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:16:02Z","timestamp":1753884962281,"version":"3.41.2"},"reference-count":57,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Image Grap."],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:p> Content-based image retrieval (CBIR) is a technique for images retrieval based on their visual features, i.e. induced by their pixels. The images are, classically, described by the image feature vectors. Those vectors reflect the texture, color or a combination of them. The accuracy of the CBIR system is highly influenced by the (i) definition of the image feature vector describing the image, (ii) indexing and (iii) retrieval process. In this paper, we propose a new CBIR system entitled ISE (Image Search Engine). Our ISE system defines the optimum combination of color and texture features as an image feature vector, including the Particle Swarm Optimization (PSO) algorithm and employing an Interactive Genetic Approach (GA) for the indexing process. The performance analysis shows that our suggested PCM (Proposed Combination Method) upgrades the average precision metric from 66.6% to 89.30% for the \u201cFood\u201d category color histogram, from 77.7% to 100% concerning CCVs (Color Coherence Vectors) for the \u201cFlower\u201d category and from 58% to 87.65% regarding the DCD (Dominant Color Descriptor) for the \u201cBuilding\u201d category using the Corel dataset. Besides, our ISE system showcases an average precision of 98.23%, which is significantly higher than other CBIR systems presented in related works. <\/jats:p>","DOI":"10.1142\/s0219467821500558","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T02:18:28Z","timestamp":1615774708000},"source":"Crossref","is-referenced-by-count":3,"title":["A New Content-Based Image Retrieval System Using Deep Visual Features"],"prefix":"10.1142","volume":"21","author":[{"given":"Mohamed","family":"Hamroun","sequence":"first","affiliation":[{"name":"Department of Computer Science, XLIM. UMR CNRS 7252, University of Limoges, 123 Avenue Albert Thomas, 87060 Limoges Cedex, France"}]},{"given":"Karim","family":"Tamine","sequence":"additional","affiliation":[{"name":"Department of Computer Science, XLIM. UMR CNRS 7252, University of Limoges, 123 Avenue Albert Thomas, 87060 Limoges Cedex, France"}]},{"given":"Frederic","family":"Claux","sequence":"additional","affiliation":[{"name":"Department of Computer Science, XLIM. UMR CNRS 7252, University of Limoges, 123 Avenue Albert Thomas, 87060 Limoges Cedex, France"}]},{"given":"Mourad","family":"Zribi","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Informatique Signal et Image de la C\u00f4te d\u2019Opale (LISIC), Department of Computer Science, University of Littoral, Maison de la Recherche Blaise Pascal, 50 rue Ferdinand Buisson, BP 719, 62228 Calais Cedex, France"}]}],"member":"219","published-online":{"date-parts":[[2021,3,12]]},"reference":[{"key":"S0219467821500558BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.10.009"},{"key":"S0219467821500558BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcm.2010.11.044"},{"key":"S0219467821500558BIB003","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2014.0769"},{"key":"S0219467821500558BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/j.csi.2010.03.004"},{"key":"S0219467821500558BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/ACV.1996.572008"},{"key":"S0219467821500558BIB006","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2011.0453"},{"key":"S0219467821500558BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2008.2001357"},{"key":"S0219467821500558BIB008","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2014.0895"},{"key":"S0219467821500558BIB009","doi-asserted-by":"publisher","DOI":"10.1109\/AISP.2015.7123522"},{"key":"S0219467821500558BIB010","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2012.0203"},{"volume-title":"Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence","year":"1975","author":"Holland J. 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