Work

The use of co-occurrence patterns in single image based food portion estimation

Year: 2017

Type: conference-paper

Abstract: Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. Food portions estimation is a challenging problem as food preparation and consumption process pose large variations on food shapes and appearances. We use geometric model based technique to estimate food portions and further improve estimation accuracy using co-occurrence patterns. We estimate the food portion co-occurrence patterns from food images we collected from dietary studies using the mobile Food Record (mFR) system we developed. Co-occurrence patterns is used as prior knowledge to refine portion estimation results. We show that the portion estimation accuracy has been improved when incorporating the co-occurrence patterns as contextual information.

Source: IEEE Global Conference on Signal and Information Processing

DOI: 10.1109/globalsip.2017.8308685

Authors: Shaobo Fang, Fengqing Zhu, Carol J Boushey, Edward J Delp

Institutions: Purdue University West Lafayette, 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