Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen.

由于食物图像识别带来的挑战性问题，食物图像的易收集性及其在健康和休闲方面的众多应用，最近引起了许多研究者的关注。在实际应用中，有必要分析和识别成千上万种不同的食物。为此，我们提出了一种基于类层次结构的新颖预测方案，该方案考虑了平面分类器之外的局部分类器。为了决定使用哪种方法，我们定义了不同的标准，同时考虑了从“儿童”分类器估计的认知不确定性分析和从“父母”分类器进行的预测。我们使用三种不确定性估计方法对我们的提案进行评估，并在两个公共食品数据集上进行了测试。