SARI: A Stage-aware Recognition Method for Ingredients Changing Appearance in Cooking Image Sequences

• Item type: Trans(1)
• 公開日: 2025-01-28
• タイトル / 言語: ja
• タイトル: SARI: A Stage-aware Recognition Method for Ingredients Changing Appearance in Cooking Image Sequences
• タイトル / 言語: en
• タイトル: SARI: A Stage-aware Recognition Method for Ingredients Changing Appearance in Cooking Image Sequences
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [研究論文] multimedia, food recognition, cooking, recipe data, datasets
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Kyoto University
• 著者所属: The University of Tokyo
• 著者所属: Kyoto University
• 著者所属(英): en
• 著者所属(英): Kyoto University
• 著者所属(英): en
• 著者所属(英): The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Kyoto University
• 著者名: Yixin,Zhang; Yoko,Yamakata; Keishi,Tajima
• 著者名(英): Yixin Zhang; Yoko Yamakata; Keishi Tajima
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Recognizing ingredients in cooking images is a challenging task due to the significant visual changes that ingredients undergo throughout the cooking process. As ingredients are prepared, cooked, and served, their appearances vary greatly between the beginning, intermediate, and finishing stages. Traditional object recognition methods, which assume constant object appearances, struggle with this variability and are often not good at accurately identifying ingredients at different cooking stages. To address this challenge, we propose a stage-aware recognition method specifically designed for dynamically changing ingredients in cooking images. Our approach introduces two techniques: 1. Stage-Wise Model Learning: This technique involves training separate models for each stage of the cooking process. By adapting models to specific stages, we can better capture the distinct visual characteristics of ingredients as their appearances change. 2. Stage-Aware Curriculum Learning: This technique begins training with data from the beginning cooking stages and progressively incorporates data from later stages. This gradual approach helps the model adapt to the evolving appearances of ingredients. Our experimental results, using our published dataset, demonstrate that our stage-aware methods significantly outperform models trained without stage considerations, achieving higher accuracy in ingredient recognition.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.33(2025) (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Recognizing ingredients in cooking images is a challenging task due to the significant visual changes that ingredients undergo throughout the cooking process. As ingredients are prepared, cooked, and served, their appearances vary greatly between the beginning, intermediate, and finishing stages. Traditional object recognition methods, which assume constant object appearances, struggle with this variability and are often not good at accurately identifying ingredients at different cooking stages. To address this challenge, we propose a stage-aware recognition method specifically designed for dynamically changing ingredients in cooking images. Our approach introduces two techniques: 1. Stage-Wise Model Learning: This technique involves training separate models for each stage of the cooking process. By adapting models to specific stages, we can better capture the distinct visual characteristics of ingredients as their appearances change. 2. Stage-Aware Curriculum Learning: This technique begins training with data from the beginning cooking stages and progressively incorporates data from later stages. This gradual approach helps the model adapt to the evolving appearances of ingredients. Our experimental results, using our published dataset, demonstrate that our stage-aware methods significantly outperform models trained without stage considerations, achieving higher accuracy in ingredient recognition.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.33(2025) (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464847
• 書誌情報: 情報処理学会論文誌データベース（TOD）; 巻 18, 号 1, 発行日 2025-01-28
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7799
• 出版者 / 言語: ja
• 出版者: 情報処理学会