A novel study on visible ingredient identification in food images for food computing

用于食品计算的食品图像中可见成分识别的新颖研究

• 批准号: 22K12095
• 负责人: 戴 瑩
• 金额: $ 2.66万
• 依托单位: Iwate Prefectural University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22K12095/
• 关键词: ingredient recognition; food image; ingredient segmentation; decision-making; food recognition; deep learning; food computing

Despite of remarkable advances in computer vision and machine learning, food image recognition is still very challenging. It is difficult for machine to identify visible ingredients in the food images, because the shapes of the same ingredients may have significant variability, while they are often visually similar to those from the other ingredient categories. In this research, we focus on solving the above issues to realize the recognition of visible ingredients in food images, and validate the effectiveness and efficiency of the proposed methods, so as to contribute to exploiting the applications and services in the fields of health, medicine, cooking, nutrition, and the related areas.Firstly, we has proposed a new ingredient hierarchical structure for classification based on 農林水産省の生鮮食品品質表示基準, which was used to build a benchmark of food ingredients dataset. Secondly, we have developed a novel approach for segmenting visible ingredients in food images by utilizing a single-ingredient classification model. Thirdly, the above segments have been recognized by introducing a decision-making scheme. The experimental results have shown that the combination of the methods of locating and sliding windows improves the average of F1 scores significantly for the ingredients recognition.

Despite of remarkable advances in computer vision and machine learning, food image recognition is still very challenging. It is difficult for machine to identify visible ingredients in the food images, because the shapes of the same ingredients may have significant variability, while they are often visually similar to those from the other ingredient categories. In this research, we focus on solving the above issues to realize the recognition of visible ingredients in food images, and validate the effectiveness and efficiency of the proposed methods, so as to contribute to exploiting the applications and services in the fields of health, medicine, cooking, nutrition, and the related areas.Firstly, we have proposed a new ingredient hierarchical structure for classification based on the Ministry of Agriculture, Forestry and Fisheries Fresh Food Quality Indication Benchmark, which was used to build a benchmark of food ingredients dataset. Secondly, we have developed a novel approach for segmenting visible ingredients in food images by utilizing a single-ingredient classification model. Thirdly, the above segments have been recognized by introducing a decision-making scheme. The experimental results have shown that the combination of the methods of locating and sliding windows improves the average of F1 scores significantly for the ingredients recognition.