Zhu, Z.; Dai, Y. A New CNN-Based Single-Ingredient Classification Model and its Application in Food Image Segmentation. J. Imaging2023, 9, 205.
Zhu, Z.; Dai, Y. A New CNN-Based Single-Ingredient Classification Model and its Application in Food Image Segmentation. J. Imaging 2023, 9, 205.
Zhu, Z.; Dai, Y. A New CNN-Based Single-Ingredient Classification Model and its Application in Food Image Segmentation. J. Imaging2023, 9, 205.
Zhu, Z.; Dai, Y. A New CNN-Based Single-Ingredient Classification Model and its Application in Food Image Segmentation. J. Imaging 2023, 9, 205.
Abstract
It is important for food recognition to separate each ingredient within a food image at the pixel level. In this paper, we propose a new approach to segment ingredients by utilizing a CNN-based Single-Ingredient Classification Model. In detail, we firstly introduce a standardized biological-based hierarchical ingredient structure and construct a single-ingredient image dataset based on this structure. Then, we build a single-ingredient classification model based on a novel convolutional neural network (CNN) architecture that utilizes an attention mechanism. Afterwards, we propose a new framework for segmentation using the above single-ingredient classification model as the backbone. In this framework, two methods are involved in segmenting ingredients in the food images. We introduce five evaluation metrics (IoU, Dice, Purity, Entirety, Loss of GTs) to assess the performance of ingredient segmentation in terms of ingredient classification. Extensive experiments demonstrate the effectiveness of the proposed method, achieving an maximal mIoU of 0.65, mDice of 0.77, mPurity of 0.83, mEntirety of 0.80, and mLoGTs of 0.06 on the FoodSeg103 dataset. The results confirm that our CNN-based architecture achieves higher segmentation performance compared to ResNet18 and EfficientNet-B0 when used as the backbone for ingredient segmentation. We believe that our ingredient segmentation approach lays the foundation for subsequent ingredient recognition.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
