International Conference on Image Analysis and Processing

ICIAP 2015: New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops pp 458-465

Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks

  • Stergios Christodoulidis
  • Marios Anthimopoulos
  • Stavroula Mougiakakou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of already segmented food items in meal images. The method uses a 6-layer deep convolutional neural network to classify food image patches. For each food item, overlapping patches are extracted and classified and the class with the majority of votes is assigned to it. Experiments on a manually annotated dataset with 573 food items justified the choice of the involved components and proved the effectiveness of the proposed system yielding an overall accuracy of 84.9%.

Keywords

Food recognition Convolutional neural networks Dietary management Machine learning 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Stergios Christodoulidis
    • 1
    • 2
  • Marios Anthimopoulos
    • 1
    • 3
  • Stavroula Mougiakakou
    • 1
    • 4
  1. 1.ARTORG Center for Biomedical Engineering ResearchUniversity of BernBernSwitzerland
  2. 2.Graduate School of Cellular and Biomedical SciencesUniversity of BernBernSwitzerland
  3. 3.Department of Emergency MedicineBern University HospitalBernSwitzerland
  4. 4.Department of Endocrinology, Diabetes and Clinical NutritionBern University HospitalBernSwitzerland

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