  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.
