Work

Food image analysis: Segmentation, identification and weight estimation

Year: 2013

Type: conference-paper

Abstract: We are developing a dietary assessment system that records daily food intake through the use of food images taken at a meal. The food images are then analyzed to extract the nutrient content in the food. In this paper, we describe the image analysis tools to determine the regions where a particular food is located (image segmentation), identify the food type (feature classification) and estimate the weight of the food item (weight estimation). An image segmentation and classification system is proposed to improve the food segmentation and identification accuracy. We then estimate the weight of food to extract the nutrient content from a single image using a shape template for foods with regular shapes and area-based weight estimation for foods with irregular shapes.

Source: IEEE International Conference on Multimedia and Expo (ICME)

DOI: 10.1109/icme.2013.6607548

Authors: Ye He, Chang Xu, Nitin Khanna, Carol J. Boushey, Edward J. Delp

Institutions: Purdue University West Lafayette, Graphic Era University, University of Hawaii Cancer Center

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: green

Funders: National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases