DietCam: Multiview Food Recognition Using a Multikernel SVM.
Level V
Where this comes from
- Record sourced from PubMed, PMID 25850095.
- Also identified by DOI 10.1109/JBHI.2015.2419251.
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Abstract
Food recognition is a key component in evaluation of everyday food intakes, and its challenge is due to intraclass variation. In this paper, we present an automatic food classification method, DietCam, which specifically addresses the variation of food appearances. DietCam consists of two major components, ingredient detection and food classification. Food ingredients are detected through a combination of a deformable part-based model and a texture verification model. From the detected ingredients, food categories are classified using a multiview multikernel SVM. In the experiment, DietCam presents reliability and outperformance in recognition of food with complex ingredients on a database including 15,262 food images of 55 food types.
Medical subject headings
- Food
- Image Processing, Computer-Assisted
- Pattern Recognition, Automated
- Support Vector Machine