{"_buckets": {"deposit": "f772fe4e-2e8e-437c-ab97-ce086c04a79c"}, "_deposit": {"id": "21705", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21705"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021705", "sets": ["314"]}, "author_link": ["64357", "64358", "64359", "64360", "64361", "64362", "64363", "64364", "64365", "64366", "64367", "64368", "64369", "64370"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "Ingredients Estimation in a Dish by Learning from a Large Number of Recipes with Images", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2013-03", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "474", "bibliographicPageEnd": "120", "bibliographicPageStart": "115", "bibliographicVolumeNumber": "112", "bibliographic_titles": [{"bibliographic_title": "電子情報通信学会技術研究報告. MVE, マルチメディア・仮想環境基礎", "bibliographic_titleLang": "ja"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "本報告では,入力された料理画像からその料理に含まれる素材を推定する手法を提案する.近年,日々の食事を記録することで個人の食事の傾向や好みを分析し,健康状態の改善を支援するサービスが増えている.このようなサービスの実現を目指した従来の研究では,食事画像から料理名を推定することで料理のカロリーを推定しようとしている.しかし,実際には同じ種類の料理でも使われる素材の種類が異なる場合も多く,料理の種類を認識するだけでは不十分である.そこで本研究では,個人が撮影した料理画像から,その料理に含まれる素材を推定する手法を提案する.料理には素材の色や形状の特徴が現れることが多いため,料理画像から画像特徴量を抽出して学習し,識別器を構築する.評価実験の結果,すべての識別器で識別性能がランダムな識別を超え,有効性を確認した.", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "In this report, we propose a method for estimating the ingredients contained in the dish in an input food image. Recently, the number of services that analyzes the preferences of an individual by recording daily meals, for improving of health, is increasing. Conventional works that try to realize such services, estimate the calorie from the name of the dish detected from the image. However, in practice, different ingredients may be used in the same kind of dishes, so only recognizing the type of a dish is insufficient. Therefore, we propose a method for estimating the ingredients in the dish in an input food image. Since the color and the shape features of an ingredient tend to appear in a dish, the proposed method extracts image feature from the food image and builds classifiers. As a result of an evaluation experiments, we confirmed that the performance of the classifier exceeds random classification.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_description_5": {"attribute_name": "内容記述", "attribute_value_mlt": [{"subitem_description": "IEICE Technical Report;IMQ2012-55,IEICE Technical Report;IE2012-159,IEICE Technical Report;MVE2012-116,IEICE Technical Report;WIT2012-65", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "URI", "subitem_identifier_uri": "http://ci.nii.ac.jp/naid/110009713334/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/23854"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "一般社団法人電子情報通信学会", "subitem_publisher_language": "ja"}]}, "item_10_relation_40": {"attribute_name": 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