title: COMBINING GLOBAL AND LOCAL FEATURES FOR FOOD IDENTIFICATION IN DIETARY ASSESSMENT.
abstract: Many chronic diseases, such as heart diseases, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing methods to use image analysis tools for the identification and quantification of food consumed at a meal. In this paper we describe a new approach to food identification using several features based on local and global measures and a "voting" based late decision fusion classifier to identify the food items. Experimental results on a wide variety of food items are presented.
authors.affiliation: Video and Image Processing Lab ( VIPER ), School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.
authors.forename: Marc
authors.initials: M
authors.lastname: Bosch
authors.affiliation: Video and Image Processing Lab ( VIPER ), School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.
authors.forename: Fengqing
authors.initials: F
authors.lastname: Zhu
authors.affiliation: Video and Image Processing Lab ( VIPER ), School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.
authors.forename: Nitin
authors.initials: N
authors.lastname: Khanna
authors.affiliation: Department of Foods and Nutrition, Purdue University, West Lafayette, IN 47907, USA.
authors.forename: Carol J
authors.initials: CJ
authors.lastname: Boushey
authors.affiliation: Video and Image Processing Lab ( VIPER ), School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.
authors.forename: Edward J
authors.initials: EJ
authors.lastname: Delp
keywords: Feature extraction; image analysis; image texture; object recognition; supervised learning
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