MLMQ-IR: Multi-label multi-query image retrieval based on the variance of Hamming distance

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dc.authorscopusid57218549439
dc.authorscopusid36705310900
dc.authorscopusid57188968069
dc.authorscopusid57210433890
dc.contributor.authorAkbacak E.
dc.contributor.authorToktas A.
dc.contributor.authorErkan U.
dc.contributor.authorGao S.
dc.date.accessioned2024-01-22T12:22:36Z
dc.date.available2024-01-22T12:22:36Z
dc.date.issued2024
dc.departmentKMÜen_US
dc.description.abstractImage retrieval (IR) methods extract the most relevant images to the query images from an image database. The existing IR methods, which retrieve images with a low degree of similarity, use computationally expensive approaches. In this study, a novel Multi-Label Multi-Query IR (MLMQ-IR) method based on the variance of Hamming distance is presented for the query of multiple images having multiple labels. The MLMQ-IR uses deep learning-based hashing code generation with ResNet50 structure. The variance evaluates the minimum variation of the distances between the query and database images, providing the trade-off images according to the center of Pareto space. Moreover, the MLMQ-IR exploits a new Triple Loss Multi-Label Hashing (TLMH) depending on binary cross-entropy loss and bit-balance loss functions. The MLMQ-IR is compared with recent multi-label and multi-query methods through MIRFLICKR-25 K, MS-COCO and NUS-WIDE datasets in terms of three well-known metrics, and the methods are ranked with succussed sorting. As a result, the MLMQ-IR method has the best avg. mean rank with 1.86. The results manifest that the MLMQ-IR provides the most similar retrieved images to the query images owing to utilizing the variance which is the efficient and fast IR approach. © 2023 Elsevier B.V.en_US
dc.description.sponsorshipThe numerical calculations reported in this paper were fully performed at Bursa Technical University High-Performance Computing Lab.en_US
dc.identifier.doi10.1016/j.knosys.2023.111193
dc.identifier.issn09507051
dc.identifier.scopus2-s2.0-85177215352
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.knosys.2023.111193
dc.identifier.urihttps://hdl.handle.net/11492/8062
dc.identifier.volume283en_US
dc.identifier.wosWOS:001165684500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier B.V.en_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzkmusnmz
dc.subjectCNNen_US
dc.subjectDeep learningen_US
dc.subjectHash codeen_US
dc.subjectImage retrievalen_US
dc.subjectMulti-label image retrievalen_US
dc.subjectMulti-query image retrievalen_US
dc.subjectVarianceen_US
dc.subjectEconomic and social effectsen_US
dc.subjectHamming distanceen_US
dc.subjectHash functionsen_US
dc.subjectImage retrievalen_US
dc.subjectQuery processingen_US
dc.subjectDeep learningen_US
dc.subjectHash codeen_US
dc.subjectImage databaseen_US
dc.subjectLabel imagesen_US
dc.subjectMulti-label image retrievalen_US
dc.subjectMulti-labelsen_US
dc.subjectMulti-query image retrievalen_US
dc.subjectQuery imagesen_US
dc.subjectRetrieval methodsen_US
dc.subjectVarianceen_US
dc.subjectDeep learningen_US
dc.titleMLMQ-IR: Multi-label multi-query image retrieval based on the variance of Hamming distanceen_US
dc.typeArticle

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