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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A New CNN-Based Single-Ingredient Classification Model and Its Application in Food Image Segmentation.

Ziyi Zhu¹, Ying Dai¹

¹Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate 020-0693, Japan.

Journal of Imaging

|October 27, 2023

Summary

This summary is machine-generated.

This study introduces a novel framework for food ingredient segmentation using image-level annotations, overcoming the limitations of pixel-level datasets. The method effectively segments ingredients, advancing food recognition capabilities.

Keywords:

CNN architecture evaluation metrics food ingredient segmentation hierarchical multi-level learning single-ingredient classification model

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Area of Science:

Computer Vision
Artificial Intelligence
Food Science

Background:

Pixel-level annotation for food ingredient segmentation is labor-intensive and time-consuming.
Existing methods struggle with the complexity and cost of creating detailed food datasets.

Purpose of the Study:

To develop an efficient framework for food ingredient segmentation using image-level annotations.
To reduce the dependency on extensive pixel-level data for training segmentation models.

Main Methods:

A standardized biological-based hierarchical ingredient structure was introduced.
A single-ingredient classification model was trained on an image-level annotated dataset.
Feature maps from the classification model were utilized for ingredient segmentation.

Main Results:

The proposed framework achieved significant results on the FoodSeg103 dataset.
Key metrics include mIoU of 0.65, mDice of 0.77, mPurity of 0.83, mEntirety of 0.80, and mLoGTs of 0.06.
The method demonstrates effectiveness in segmenting ingredients from food images.

Conclusions:

The developed framework offers a viable alternative to pixel-level annotation for food ingredient segmentation.
This approach provides a foundation for enhanced food recognition systems.
The study highlights the potential of leveraging image-level data for complex segmentation tasks.