Abstract: In this study, an automatic food recognition system using multi class support vector machine classifier is presented. For classification of the food item, four features are considered viz. Size, shape, color and texture. In the works carried out previously, only single food item types were considered but in this study mixed foods are also taken into account. To detect mixed foods, Region Of Interest (ROI) method is used. Since, four important features are considered for classification, this system provides high accuracy. The system is built for food image processing and uses nutritional fact Table for calorie measurement. Three techniques are adopted to extract features. They are: Scale Invariant Feature Transformation (SIFT) method-extracts shape of images. Gabor method-extracts the texture feature. Color histogram-extracts color features of an image. After extracting these features, the image is classified using multiclass SVM to identify the class of provided food image. By finding, the area and volume of the food samples, calorie values are calculated. The multiclass SVM methods like one-against-one and one-against-all methods are compared with binary SVM against the food samples. Furthermore, the results show that the proposed methods become favorable as the number of classes are increased
P.V. Elvizhy, A. Kannan, S. Abayambigai and A.P. Sindhuja, 2016. Food Recognition and Calorie Estimation Using Multi-Class SVM Classifier. Asian Journal of Information Technology, 15: 866-875.