Work

Multilevel Segmentation for Food Classification in Dietary Assessment.

Year: 2011

Type: conference-paper

Abstract: Given a dataset of images, we seek to automatically identify and locate perceptually similar objects. We combine two ideas to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of each image and then learning the object class by combining different segmentations to generate optimal segmentation. We demonstrate that the proposed method can be used as part of a new dietary assessment tool to automatically identify and locate the foods in a variety of food images captured during different user studies.

Source: PubMed

Authors: Fengqing Zhu, Marc Bosch, Nitin Khanna, Carol J Boushey, Edward J Delp

Institution: Purdue University West Lafayette

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

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

Awards: Improving Dietary Assessment Methods Using the Cell Phone and Digital Imaging, Improving Diet Assessment in Adolescents

OpenAlex ID: W2101886764

FWCI: 4.057

Cites: 20

Cited by: 38

Related to: 10