IEEE Trans Image Process. 2021;30:1514-1526. doi: 10.1109/TIP.2020.3045639. Epub 2020 Dec 31.
Food recognition has captured numerous research attention for its importance for health-related applications. The existing approaches mostly focus on the categorization of food according to dish names, while ignoring the underlying ingredient composition. In reality, two dishes with the same name do not necessarily share the exact list of ingredients. Therefore, the dishes under the same food category are not mandatorily equal in nutrition content. Nevertheless, due to limited datasets available with ingredient labels, the problem of ingredient recognition is often overlooked. Furthermore, as the number of ingredients is expected to be much less than the number of food categories, ingredient recognition is more tractable in the real-world scenario. This paper provides an insightful analysis of three compelling issues in ingredient recognition. These issues involve recognition in either image-level or region level, pooling in either single or multiple image scales, learning in either single or multi-task manner. The analysis is conducted on a large food dataset, Vireo Food-251, contributed by this paper. The dataset is composed of 169,673 images with 251 popular Chinese food and 406 ingredients. The dataset includes adequate challenges in scale and complexity to reveal the limit of the current approaches in ingredient recognition.
食物识别因其在与健康相关的应用中的重要性而引起了众多研究的关注。现有的方法主要侧重于根据菜名对食物进行分类，而忽略了潜在的成分组成。实际上，两个同名的菜不一定具有完全相同的成分列表。因此，同一食品类别下的菜肴在营养成分上不一定是等同的。然而，由于具有成分标签的有限数据集，成分识别问题经常被忽视。此外，由于成分的数量预计比食品类别的数量少得多，因此在现实场景中，成分识别更加可行。本文对成分识别中的三个引人注目的问题进行了深入分析。这些问题涉及图像级别或区域级别的识别、单尺度或多尺度图像的池化、单任务或多任务学习。分析是在一个由本文贡献的大型食品数据集 Vireo Food-251 上进行的。该数据集由 169673 张图像组成，涵盖了 251 种流行的中国菜和 406 种成分。该数据集在规模和复杂性方面具有足够的挑战，可以揭示当前成分识别方法的局限性。
