{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T06:22:31Z","timestamp":1758349351955,"version":"3.44.0"},"reference-count":38,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:p> Traditional content-based image retrieval (CBIR) searches a database for pictures that match a single, unannotated query image. A comprehensive (or worldwide) perspective of the picture is essential for this kind of search. However, the intended picture content is frequently not global rather regional. Computer vision, pattern recognition, and CBIR\u00a0are just a few of the applications that have long faced the \u201csemantic gap\u201d issue: how to bridge the gap between human perception of low-level semantic concepts and machine collection of high-level image pixels. In light of the recent achievements in deep learning research, there is hope for bridging the semantic gap. Therefore, the deep learning-assisted localized content-based\u00a0image retrieval framework (DL-LCBIRF) was suggested in this study to rank photos in the database according to a similarity metric dependent upon specific areas inside the image. The first layer consists of \u201cgeneric\u201d descriptors that stand in for groups of feature vectors that are comparable and rotationally invariant. The combined probability of the frequencies of the \u201cgeneric\u201d descriptions over neighborhoods makes up the second layer. The proposed DL-LCBIR uses labelled images combined with a multiple-instance learning algorithm (MILA) to locate the target item and adjust the feature weights. This multi-modal probability is shown as a collection of \u201cspatial frequency\u201d clusters. It augments rotationally invariant statistical spatial constraints. In addition to enhancing the model\u2019s performance, choosing a unique structure determines the model\u2019s distinguishing features, which are common in good cases and seldom in negative ones. <\/jats:p>","DOI":"10.1142\/s0218001425520196","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T06:02:59Z","timestamp":1752732179000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Assisted Localized Content-based\u00a0Image Retrieval Framework with Multiple-Instance Learning Algorithm"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2755-3691","authenticated-orcid":false,"given":"P.","family":"Arulmozhi","sequence":"first","affiliation":[{"name":"Department of Information Technology, Anna University, Dhanalakshmi Srinivasan Engineering College, Perambalur, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4957-1843","authenticated-orcid":false,"given":"R.","family":"Gopi","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Anna University, Dhanalakshmi Srinivasan Engineering College, Perambalur, India"}]}],"member":"219","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"key":"S0218001425520196BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-023-08087-y"},{"issue":"2","key":"S0218001425520196BIB002","first-page":"873","volume":"42","author":"Alzubi O. 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