{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00178763","sets":["1164:4619:9026:9159"]},"path":["9159"],"owner":"11","recid":"178763","title":["大規模なデータセットの構築のための画像のフィルタリング手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-05-03"},"_buckets":{"deposit":"4f4b6d04-bc5f-40a5-b8a0-8a165cab84bd"},"_deposit":{"id":"178763","pid":{"type":"depid","value":"178763","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"大規模なデータセットの構築のための画像のフィルタリング手法","author_link":["383789","383792","383791","383793","383790"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"大規模なデータセットの構築のための画像のフィルタリング手法"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"卒論スポットライトセッション","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2017-05-03","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪府立大学大学院工学研究科"},{"subitem_text_value":"大阪府立大学大学院工学研究科"},{"subitem_text_value":"大阪府立大学大学院工学研究科"},{"subitem_text_value":"大阪府立大学大学院工学研究科"},{"subitem_text_value":"大阪府立大学大学院工学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Engineering, Osaka Prefecture University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Osaka Prefecture University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Osaka Prefecture University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Osaka Prefecture University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Osaka Prefecture University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing 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祐輝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"森本, 直之"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山田, 良博"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"岩村, 雅一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"黄瀬, 浩一"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"一般物体認識において，高い認識精度を実現することは重要である．高い認識精度を実現するには，識別器に学習させるデータセットに正しくラベル付けされている必要がある．また，あるカテゴリに属する物体には，外見のパターンがいくつか存在することから，多様なデータを含んだ大規模なデータセットが必要となる．このようなデータセットを作る上で，大量にデータを集めることは比較的容易であるが，正しくラベル付けするには膨大なコストがかかり，容易でない．そこで本稿では，ラベル付けする手間を省き，多様なデータを含む大規模なデータセットを構築するための画像のフィルタリング手法を提案する．実験結果では，集める対象となる画像を約 52% 含む全体で 21,786 枚のデータセットをフィルタリングすることで，その画像を約 64 従って，提案手法では，集める対象となる画像の割合を約 12% 上げることができた．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2017-05-03","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"30","bibliographicVolumeNumber":"2017-CVIM-207"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":178763,"updated":"2025-01-20T05:00:56.698411+00:00","links":{},"created":"2025-01-19T00:48:03.898782+00:00"}