{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T05:17:33Z","timestamp":1740028653791,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Learning effective feature representations and similarity measures are critical to the performance of a CBIR. Although various techniques have been proposed, it remains one of the most challenging problems in CBIR, which is mainly due to &amp;ldquo;semantic gap&amp;rdquo; issue that exists between low-level image pixels captured by machine and high-level semantic concepts perceived by human. One of the most important advances in machine learning is known as &amp;ldquo;deep learning&amp;rdquo; that attempts to model high-level abstractions in data by employing deep architectures composed of multiple non-linear transformations. We can improve CBIR using the state-of-the-art deep learning techniques for learning feature representations and similarity measures. Deep Neural Networks have recently shown great performance on image classification.<\/jats:p>","DOI":"10.3233\/978-1-61499-882-2-71","type":"book-chapter","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T15:30:51Z","timestamp":1739979051000},"source":"Crossref","is-referenced-by-count":0,"title":["CBIR on Big Data by Use of Deep Learning"],"prefix":"10.3233","author":[{"family":"Saeidi Mahmoud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Ahmadi Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Advances in Parallel Computing","Big Data and HPC: Ecosystem and Convergence"],"original-title":[],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T15:55:37Z","timestamp":1739980537000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-881-5&spage=71&doi=10.3233\/978-1-61499-882-2-71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-882-2-71","relation":{},"ISSN":["0927-5452"],"issn-type":[{"value":"0927-5452","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}