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Saliency-Aware Class-Agnostic Food Image Segmentation

Authors:

Sri Kalyan Yarlagadda,

Daniel Mas Montserrat,

Carol J. Boushey,

Deborah A. Kerr,

Fengqing ZhuAuthors Info & Claims

ACM Transactions on Computing for Healthcare , Volume 2, Issue 3

Article No.: 19, Pages 1 - 17

https://doi.org/10.1145/3440274

Published: 15 July 2021 Publication History

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Abstract

Advances in image-based dietary assessment methods have allowed nutrition professionals and researchers to improve the accuracy of dietary assessment, where images of food consumed are captured using smartphones or wearable devices. These images are then analyzed using computer vision methods to estimate energy and nutrition content of the foods. Food image segmentation, which determines the regions in an image where foods are located, plays an important role in this process. Current methods are data dependent and thus cannot generalize well for different food types. To address this problem, we propose a class-agnostic food image segmentation method. Our method uses a pair of eating scene images, one before starting eating and one after eating is completed. Using information from both the before and after eating images, we can segment food images by finding the salient missing objects without any prior information about the food class. We model a paradigm of top-down saliency that guides the attention of the human visual system based on a task to find the salient missing objects in a pair of images. Our method is validated on food images collected from a dietary study that showed promising results.

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Konstantakopoulos FGeorga EFotiadis D(2024)A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence SystemsIEEE Reviews in Biomedical Engineering10.1109/RBME.2023.328314917(136-152)Online publication date: 2024
https://doi.org/10.1109/RBME.2023.3283149
Yang CGuan ZMa W(2024)Salient Object Detection Based on High-level Semantic Guidance and Multi-modal Interaction2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)10.1109/IAEAC59436.2024.10504007(1819-1824)Online publication date: 15-Mar-2024
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Index Terms

Saliency-Aware Class-Agnostic Food Image Segmentation
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Interest point and salient region detections
        Object detection

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cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 2, Issue 3

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July 2021

226 pages

EISSN:2637-8051

DOI:10.1145/3476113

Editors:
Insup Lee
University of Pennsylvania, USA
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John A. Stankovic
University of Virginia, USA

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Publication History

Published: 15 July 2021

Accepted: 01 November 2020

Revised: 01 April 2020

Received: 01 July 2019

Published in HEALTH Volume 2, Issue 3

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Konstantakopoulos FGeorga EFotiadis D(2024)A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence SystemsIEEE Reviews in Biomedical Engineering10.1109/RBME.2023.328314917(136-152)Online publication date: 2024
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Yang CGuan ZMa W(2024)Salient Object Detection Based on High-level Semantic Guidance and Multi-modal Interaction2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)10.1109/IAEAC59436.2024.10504007(1819-1824)Online publication date: 15-Mar-2024
https://doi.org/10.1109/IAEAC59436.2024.10504007
YANG FGUO XLIANG SZHAO PLI S(2023)Siamese Transformer for Saliency Prediction Based on Multi-Prior Enhancement and Cross-Modal Attention CollaborationIEICE Transactions on Information and Systems10.1587/transinf.2022EDP7220E106.D:9(1572-1583)Online publication date: 1-Sep-2023
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Tang BLiu ZTan YHe Q(2023)HRTransNet: HRFormer-Driven Two-Modality Salient Object DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.320256333:2(728-742)Online publication date: Feb-2023
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Sultana JAhmed BMasud MHuq AAli MNaznin M(2023)A Study on Food Value Estimation From Images: Taxonomies, Datasets, and TechniquesIEEE Access10.1109/ACCESS.2023.327447511(45910-45935)Online publication date: 2023
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Xia XLiu WWang LSun J(2023)HSIFoodIngr-64: A Dataset for Hyperspectral Food-Related Studies and a Benchmark Method on Food Ingredient RetrievalIEEE Access10.1109/ACCESS.2023.324324311(13152-13162)Online publication date: 2023
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