10 July 2009 An automatic food recognition algorithm with both shape and texture information
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Proceedings Volume 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering; 748905 (2009) https://doi.org/10.1117/12.836650
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China
Abstract
Automatic food classification with digital images has played an important role in modern agricultural and food engineering. For this purpose, a kind of recognition algorithm for food is presented based on their shape and texture information in this paper.. By using a combination of shape and texture feature, improved mean-shift procedure is a state-of-the-art learning algorithm for multi-classification of food. The proposed method has four steps: (1) computation of a high contrast monochrome image from an optimal linear combination of RGB components of the food colour image;(2)a morphological shape detection operation is applied to detect the actual food shape from the high contrast monochrome image,some structural elements that have special forms are utilized to eliminate noise and improve detection precision; (3)a food texture is modeled by co-occurrence matrix;(4)a feature combination method is specified by food shape and texture information synthetically, then an improved mean-shift algorithm is proposed to achieve automatic food classification and recognition. The algorithm was implemented in Matlab and tested with 180 images (512×512) taken for various food with big differences. The algorithm can be applied to recognize food categories at the speed of 1.13s per image with the approval recognition rate of 97.6%. The result shows that our algorithm fully satisfies the requests of real application.
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Yu Deng, Shiyin Qin, Yunjie Wu, "An automatic food recognition algorithm with both shape and texture information", Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 748905 (10 July 2009); doi: 10.1117/12.836650; https://doi.org/10.1117/12.836650
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