TY  - JOUR
T1  - Food Recognition and Calorie Estimation Using Multi-Class SVM Classifier
AU - Elvizhy, P.V. AU - Kannan, A. AU - Abayambigai, S. AU - Sindhuja, A.P. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 5
SP  - 866
EP  - 875
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.866.875
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.866.875
KW  - Food recognition
KW  -image classification
KW  -feature extraction
KW  -segmentation
KW  -calorie measurement
AB  - 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
ER  -