Recognizing food images presents unique challenges due to the variable
spatial layout and shape changes of ingredients with different cooking and
cutting methods. This study introduces an advanced approach for recognizing
ingredients segmented from food images. The method localizes the candidate
regions of the ingredients using the locating and sliding window techniques.
Then, these regions are assigned into ingredient classes using a CNN
(Convolutional Neural Network)-based single-ingredient classification model
trained on a dataset of single-ingredient images. To address the challenge of
processing speed in multi-ingredient recognition, a novel model pruning method
is proposed that enhances the efficiency of the classification model.
Subsequently, the multi-ingredient identification is achieved through a
decision-making scheme, incorporating two novel algorithms. The
single-ingredient image dataset, designed in accordance with the book entitled
"New Food Ingredients List FOODS 2021", encompasses 9982 images across 110
diverse categories, emphasizing variety in ingredient shapes. In addition, a
multi-ingredient image dataset is developed to rigorously evaluate the
performance of our approach. Experimental results validate the effectiveness of
our method, particularly highlighting its improved capability in recognizing
multiple ingredients. This marks a significant advancement in the field of food
image analysis.

本研究提出了一种改进的方法来识别从食物图像中分割出的成分，使用定位和滑动窗口技术定位成分的候选区域，并使用基于 CNN 的单成分分类模型将这些区域分配到成分类别中。为了解决多成分识别中的处理速度挑战，提出了一种新的模型修剪方法，提高了分类模型的效率。随后，通过融合两种新算法的决策方案实现了多成分的识别。实验结果验证了我们方法的有效性，尤其突出了其在识别多个成分方面的改进能力，这在食物图像分析领域标志着重大进展。