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    Weakly supervised food image segmentation using class activation maps

    Access Status
    Fulltext not available
    Authors
    Wang, Y.
    Zhu, F.
    Boushey, Carol
    Delp, E.
    Date
    2018
    Type
    Conference Paper
    
    Metadata
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    Citation
    Wang, Y. and Zhu, F. and Boushey, C. and Delp, E. 2018. Weakly supervised food image segmentation using class activation maps, pp. 1277-1281.
    Source Title
    Proceedings - International Conference on Image Processing, ICIP
    DOI
    10.1109/ICIP.2017.8296487
    ISBN
    9781509021758
    School
    School of Public Health
    URI
    http://hdl.handle.net/20.500.11937/67520
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 IEEE. Food image segmentation plays a crucial role in image-based dietary assessment and management. Successful methods for object segmentation generally rely on a large amount of labeled data on the pixel level. However, such training data are not yet available for food images and expensive to obtain. In this paper, we describe a weakly supervised convolutional neural network (CNN) which only requires image level annotation. We propose a graph based segmentation method which uses the class activation maps trained on food datasets as a top-down saliency model. We evaluate the proposed method for both classification and segmentation tasks. We achieve competitive classification accuracy compared to the previously reported results.

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