{"created":"2023-06-20T16:46:32.809510+00:00","id":18052,"links":{},"metadata":{"_buckets":{"deposit":"24587d52-8f30-4885-a3e1-e107f8d6c493"},"_deposit":{"created_by":29,"id":"18052","owners":[29],"pid":{"revision_id":0,"type":"depid","value":"18052"},"status":"published"},"_oai":{"id":"oai:kindai.repo.nii.ac.jp:00018052","sets":["14:2667:4296"]},"author_link":["4157"],"item_8_biblio_info_21":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2016","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"4","bibliographicPageStart":"1","bibliographic_titles":[{"bibliographic_title":"科学研究費助成事業研究成果報告書 (2015)"}]}]},"item_8_description_33":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"研究成果の概要（和文）：画像・映像の中に含まれている物体の認識を行うためには，対象となる環境において大量のサンプルを取得して機械学習を行うアプローチが主流である．しかし，実際の応用場面では必ずしも常に大量の学習サンプルを獲得できる訳ではない．そこで，異なる環境で収集したデータを用いて学習した識別器を用いて新たな環境において精度の高い識別を行う手法を提案した．実環境において収集したデータを用いて検証を行ったところ提案手法が有効に動作することがわかった．\n研究成果の概要（英文）：Image- and video-based object recognition usually requires a large amount of training data for sufficient performance. However, it is not always possible to obtain such a large dataset. Hence, we propose a method for extracting useful classifiers from a set of existing classifiers that is obtained by using training data of other scenes. Combining the extracted classifiers enables us to achieve better performance than solely using training data of a new scene. Experimental results show the effectiveness of the proposed method.","subitem_description_type":"Abstract"}]},"item_8_description_36":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"研究種目：基盤研究(C);  研究期間：2013～2015;   課題番号：25330215;   研究分野：コンピュータビジョン，物体認識;   科研費の分科・細目：","subitem_description_type":"Other"}]},"item_8_description_37":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"subitem_description":"Research Paper","subitem_description_type":"Other"}]},"item_8_description_41":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_8_publisher_14":{"attribute_name":"出版者 名前","attribute_value_mlt":[{"subitem_publisher":"近畿大学"}]},"item_8_relation_11":{"attribute_name":"著者 外部リンク","attribute_value_mlt":[{"subitem_relation_name":[{"subitem_relation_name_text":"https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-25330215/"}]}]},"item_8_text_10":{"attribute_name":"著者 役割","attribute_value_mlt":[{"subitem_text_value":"研究代表者"}]},"item_8_text_7":{"attribute_name":"著者(英)","attribute_value_mlt":[{"subitem_text_language":"en","subitem_text_value":"HABE, Hitoshi"}]},"item_8_text_8":{"attribute_name":"著者 所属","attribute_value_mlt":[{"subitem_text_value":"近畿大学理工学部; 講師"}]},"item_8_text_9":{"attribute_name":"著者所属(翻訳)","attribute_value_mlt":[{"subitem_text_value":"Kindai University"}]},"item_8_version_type_12":{"attribute_name":"版","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_be7fb7dd8ff6fe43","subitem_version_type":"NA"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"波部, 斉"},{"creatorName":"ハベ, ヒトシ","creatorNameLang":"ja-Kana"}],"nameIdentifiers":[{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2016-10-17"}],"displaytype":"detail","filename":"25330215seika.pdf","filesize":[{"value":"456.3 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"25330215seika.pdf","url":"https://kindai.repo.nii.ac.jp/record/18052/files/25330215seika.pdf"},"version_id":"80783ec9-311f-4da3-a6cc-377be8a2727e"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"物体認識","subitem_subject_scheme":"Other"},{"subitem_subject":"性別推定","subitem_subject_scheme":"Other"},{"subitem_subject":"Random Forests","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"research report","resourceuri":"http://purl.org/coar/resource_type/c_18ws"}]},"item_title":"ユニバーサルクラシファイア：多様なドメインに適応する物体認識技術の実現","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ユニバーサルクラシファイア：多様なドメインに適応する物体認識技術の実現"},{"subitem_title":"Universal Classifier: Adaptive Object Recognition for Multiple Domains","subitem_title_language":"en"}]},"item_type_id":"8","owner":"29","path":["4296"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-10-17"},"publish_date":"2016-10-17","publish_status":"0","recid":"18052","relation_version_is_last":true,"title":["ユニバーサルクラシファイア：多様なドメインに適応する物体認識技術の実現"],"weko_creator_id":"29","weko_shared_id":-1},"updated":"2023-06-20T22:10:48.187719+00:00"}