A Comparative Study of Indian Food Image Classification Using K-Nearest-Neighbour and Support-Vector-Machines

  • Authors

    • Pathanjali C
    • Vimuktha E Salis
    • Jalaja G
    • Latha A
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16171
  • Food Classification, KNN (k-nearest-neighbour), SVM (Support Vector Machine), Template Matching

  • Abstract

    Food being the vital part of everyone’s lives, food detection and recognition becomes an interesting and challenging problem in computer vision and image processing. In this paper we mainly propose an automatic food detection system that detects and recognises varieties of Indian food. This paper uses a combined colour and shape features. The K-Nearest-Neighbour (KNN) and Support-Vector -Machine (SVM) classification models are used to classify the features. A comparative study on the performance of both the classification models is performed. The experimental result shows the higher efficiency of SVM classifier over KNN classifier.

     

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  • How to Cite

    C, P., E Salis, V., G, J., & A, L. (2018). A Comparative Study of Indian Food Image Classification Using K-Nearest-Neighbour and Support-Vector-Machines. International Journal of Engineering & Technology, 7(3.12), 521-525. https://doi.org/10.14419/ijet.v7i3.12.16171

    Received date: 2018-07-24

    Accepted date: 2018-07-24

    Published date: 2018-07-20