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    Analysis of food images: Features and classification

    Access Status
    Fulltext not available
    Authors
    He, Y.
    Xu, C.
    Khanna, N.
    Boushey, Carol
    Delp, E.
    Date
    2014
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    He, Y. and Xu, C. and Khanna, N. and Boushey, C. and Delp, E. 2014. Analysis of food images: Features and classification, in Proceedings of the International Conference on Image Processing (ICIP), Oct 27-30 2014, pp. 2744-2748. Paris, France: IEEE.
    Source Title
    2014 IEEE International Conference on Image Processing, ICIP 2014
    DOI
    10.1109/ICIP.2014.7025555
    ISBN
    9781479957514
    School
    School of Public Health
    URI
    http://hdl.handle.net/20.500.11937/50858
    Collection
    • Curtin Research Publications
    Abstract

    In this paper we investigate features and their combinations for food image analysis and a classification approach based on k-nearest neighbors and vocabulary trees. The system is evaluated on a food image dataset consisting of 1453 images of eating occasions in 42 food categories which were acquired by 45 participants in natural eating conditions. The same image dataset is used to test the classification system proposed in the previously reported work [1]. Experimental results indicate that using our combination of features and vocabulary trees for classification improves the food classification performance about 22% for the Top 1 classification accuracy and 10% for the Top 4 classification accuracy.

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