title: Segmentation Assisted Food Classification for Dietary Assessment.
abstract: Accurate methods and tools to assess food and nutrient intake are essential for the association between diet and health. Preliminary studies have indicated that the use of a mobile device with a built-in camera to obtain images of the food consumed may provide a less burdensome and more accurate method for dietary assessment. We are developing methods to identify food items using a single image acquired from the mobile device. Our goal is to automatically determine the regions in an image where a particular food is located (segmentation) and correctly identify the food type based on its features (classification or food labeling). Images of foods are segmented using Normalized Cuts based on intensity and color. Color and texture features are extracted from each segmented food region. Classification decisions for each segmented region are made using support vector machine methods. The segmentation of each food region is refined based on feedback from the output of classifier to provide more accurate estimation of the quantity of food consumed.
authors: School of Electrical and Computer Engineering Purdue University, West Lafayette, Indiana USA.
authors: Fengqing
authors: F
authors: Zhu
authors: Marc
authors: M
authors: Bosch
authors: Tusarebecca
authors: T
authors: Schap
authors: Nitin
authors: N
authors: Khanna
authors: David S
authors: DS
authors: Ebert
authors: Carol J
authors: CJ
authors: Boushey
authors: Edward J
authors: EJ
authors: Delp