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Comparison of Machine Learning Algorithms in Classifying Segmented Photographs of Food for Food Logging

McAllister, Patrick, Zheng, Huiru, Bond, Raymond R and Moorhead, Anne (2016) Comparison of Machine Learning Algorithms in Classifying Segmented Photographs of Food for Food Logging. In: Collaborative European Research Conference, Cork. CERC. 4 pp. [Conference contribution]

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URL: http://cerc-conf.eu/cerc/CERC2016_proceedings_v1.pdf


Obesity is increasing globally and is a major cause for concern (WHO, 2016). The main cause of obesity is a result of a high calorie/ fat diet and when the energy is not burned off through exercise, then much of the excess energy will be stored as fat around the body. Obesity is a serious threat to an individual’s health as it can contribute to a range of major chronic conditions such as heart disease, diabetes, and some cancers (National Institutes of Health, 1998). Food logging is a popular dietary management method that has been used by individuals to monitor food intake. Food logging can include the use of text or images to document intake and research has shown that food intake monitoring can promote weight loss (Wing, 2001).There has been much research in using computer vision algorithms to classify images of food for food logging. Computer vision methods can offer a convenient way for the user to document energy intake. The motivation for this work is to inform the development of an application that would allow users to use a polygonal tool to draw around the food item for classification. This work explores the efficacy classifying segmented items of food instead of entire food images.This work explores machine learning (ML) techniques and feature extraction methods to classify 27 food categories with each category containing 100 segmented images. The image dataset used for this work comprises of 27 distinct food categories gathered from other research. (Jontou et al, 2009; Bossard et al, 2014). Non-food items contained in the images were removed to promote accurate feature selection (Figure 1).

Item Type:Conference contribution (Paper)
Keywords:Obesity, nutrition, machine learning, machine vision, smart phones
Faculties and Schools:Faculty of Social Sciences > School of Communication
Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Social Sciences
Research Institutes and Groups:Institute for Research in Social Sciences > Communication
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Institute for Research in Social Sciences
ID Code:36064
Deposited By: Dr Raymond Bond
Deposited On:07 Oct 2016 14:19
Last Modified:17 Oct 2017 16:26

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