NDLI: Automatic Chinese food identification and quantity estimation

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Automatic Chinese food identification and quantity estimation

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Automatic Chinese food identification and quantity estimation

Content Provider	ACM Digital Library
Author	Liu, Shane-Ming Chang, Eugene Yeh, Che-Hua Yang, Yung-Hsiang Ho, Chia-Ju Ouhyoung, Ming Chen, Mei-Yun Wang, Shih-Han
Abstract	Computer-aided food identification and quantity estimation have caught more attention in recent years because of the growing concern of our health. The identification problem is usually defined as an image categorization or classification problem and several researches have been proposed. In this paper, we address the issues of feature descriptors in the food identification problem and introduce a preliminary approach for the quantity estimation using depth information. Sparse coding is utilized in the SIFT and Local binary pattern feature descriptors, and these features combined with gabor and color features are used to represent food items. A multi-label SVM classifier is trained for each feature, and these classifiers are combined with multi-class Adaboost algorithm. For evaluation, 50 categories of worldwide food are used, and each category contains 100 photographs from different sources, such as manually taken or from Internet web albums. An overall accuracy of 68.3% is achieved, and success at top-N candidates achieved 80.6%, 84.8%, and 90.9% accuracy accordingly when N equals 2, 3, and 5, thus making mobile application practical. The experimental results show that the proposed methods greatly improve the performance of original SIFT and LBP feature descriptors. On the other hand, for quantity estimation using depth information, a straight forward method is proposed for certain food, while transparent food ingredients such as pure water and cooked rice are temporarily excluded.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450319157
DOI	10.1145/2407746.2407775
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-11-28
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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