{"created":"2023-05-15T08:44:23.817044+00:00","id":9677,"links":{},"metadata":{"_buckets":{"deposit":"1f7c0e58-8bc6-4b6a-8395-c80c55b02b32"},"_deposit":{"created_by":13,"id":"9677","owners":[13],"pid":{"revision_id":0,"type":"depid","value":"9677"},"status":"published"},"_oai":{"id":"oai:uec.repo.nii.ac.jp:00009677","sets":["6"]},"author_link":["26088","26089","26090","26091"],"control_number":"9677","item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-08-07","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"147069","bibliographicPageStart":"147059","bibliographicVolumeNumber":"8","bibliographic_titles":[{"bibliographic_title":"IEEE Access","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"In this article, we introduce salient object detection with importance degree (SOD-ID), which is a generalized technique for salient object detection (SOD), and propose an SOD-ID method. We define SOD-ID as a technique that detects salient objects and estimates their importance degree values. Hence, it is more effective for some image applications than SOD, which is shown via examples. The definition, evaluation procedure, and data collection for SOD-ID are introduced and discussed, and we propose its evaluation metric and data preparation, whose validity is discussed with the simulation results. Moreover, we propose an SOD-ID method, which consists of three technical blocks: instance segmentation, saliency detection, and importance degree estimation. The saliency detection block is proposed based on a convolutional neural network using the results of the instance segmentation block. The importance degree estimation block is achieved using the results of the other blocks. The proposed method accurately suppresses inaccurate saliencies and estimates the importance degree for multi-object images. In the simulations, the proposed method outperformed state-of-the-art methods with respect to the F-measure for SOD; and Spearman's and Kendall rank correlation coefficients, and the proposed metric for SOD-ID.","subitem_description_type":"Abstract"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"IEEE"}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isVersionOf","subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1109/ACCESS.2020.3014886","subitem_relation_type_select":"DOI"}}]},"item_10001_relation_17":{"attribute_name":"関連サイト","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1109/ACCESS.2020.3014886","subitem_relation_type_select":"DOI"}}]},"item_10001_rights_15":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"(c) 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for   all other uses, in any current or future media, including reprinting/republishing this material for   advertising or promotional purposes, creating new collective works, for resale or redistribution to   servers or lists, or reuse of any copyrighted component of this work in other works. This is not the   published version. Please cite only the published version."}]},"item_10001_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"21693536","subitem_source_identifier_type":"ISSN"}]},"item_10001_version_type_20":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_ab4af688f83e57aa","subitem_version_type":"AM"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Umeki, Yo","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Funahashi, Isana","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoshida, Taichi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Iwahashi, Masahiro","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2020-11-18"}],"displaytype":"detail","filename":"I専攻吉田_文献No.1の著者最終稿.pdf","filesize":[{"value":"3.9 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"I専攻吉田_文献No.1の著者最終稿","url":"https://uec.repo.nii.ac.jp/record/9677/files/I専攻吉田_文献No.1の著者最終稿.pdf"},"version_id":"467b9215-4750-4b88-8ba5-ddfc5665185a"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Saliency detection","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"salient object detection","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"instance segmentation","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"convolutional neural network (CNN)","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"rank correlation metric","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Salient Object Detection With Importance Degree","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Salient Object Detection With Importance Degree","subitem_title_language":"en"}]},"item_type_id":"10001","owner":"13","path":["6"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2020-11-18"},"publish_date":"2020-11-18","publish_status":"0","recid":"9677","relation_version_is_last":true,"title":["Salient Object Detection With Importance Degree"],"weko_creator_id":"13","weko_shared_id":-1},"updated":"2023-07-21T06:41:04.220625+00:00"}