{"created":"2020-09-01T13:29:21.357134+00:00","id":3770,"links":{},"metadata":{"_buckets":{"deposit":"584724f1-126a-4037-86d7-72220001ad13"},"_deposit":{"id":"3770","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3770"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3770","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Image Database Classification and Retrieval Using Support Vector Machines (SVM)","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"In this paper, we propose a support vector machines (SVMs) method of classifying image regions hierarchically based on their semantics rather than on low-level features. First, image regions are segmented using the hill-climbing method. And then, the support vector machines classify these regions. The SVMs learn the semantics of specified classes from a database of image regions. A support vector machine was used as the classifier. We developed a new way to assign probability after multi-class SVM classification. Our approach achieved approximately 90% accuracy on a collection of images with minimal noise. A support vector machine (SVM) is used to classify the feature vectors. To reduce the computation time and improve the classification accuracy. We also developed a new way to compute probabilistic outputs from a multi-class support vector machines."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value":[]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-07-31"}],"displaytype":"preview","filename":"54080.pdf","filesize":[{"value":"264 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3770/files/54080.pdf"},"version_id":"e83d3bf0-868a-498f-808e-d5f124999056"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Fourth Local Conference on Parallel and Soft Computing","subitem_pages":"","subitem_volume":""}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"","subitem_c_date":"","subitem_conference_title":"","subitem_part":"","subitem_place":"","subitem_session":"","subitem_website":""}]},"item_1583103211336":{"attribute_name":"Books/reports/chapters","attribute_value_mlt":[{"subitem_book_title":"","subitem_isbn":"","subitem_pages":"","subitem_place":"","subitem_publisher":""}]},"item_1583103233624":{"attribute_name":"Thesis/dissertations","attribute_value_mlt":[{"subitem_awarding_university":"","subitem_supervisor(s)":[{"subitem_supervisor":""}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Thant, Wut Yee"},{"subitem_authors_fullname":"Khaing, Thiri Thitsar"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Article"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2009-12-30"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://ucsy.edu.mm/onlineresource/handle/123456789/1502"},"item_title":"Image Database Classification and Retrieval Using Support Vector Machines (SVM)","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-31","publish_status":"0","recid":"3770","relation_version_is_last":true,"title":["Image Database Classification and Retrieval Using Support Vector Machines (SVM)"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T03:56:24.603309+00:00"}