Feature Extraction using GLCM for Dietary Assessment Application

This paper offers technique for dietary assessment towards mechanically detect the type of food from various pictures captured during eating occasions. Recognition of food is complicated procedure since most of the food items are varies in shape and appearance. To achieve this task segmentation is important for labelling of food. The features of each segmented regions are extracted by capturing visual content of image. System works well on the most relevant six statistical parameters or texture features computed by using Gray Level Co-occurrence Matrix (GLCM). Then construct a feature vector to represents all feature values. The operation of classification will be performed on the basis of defined features. Experimental results on various food items are obtained. This food recognition system can be easily integrated into dietary assessment applications. By analysing food portion and size information, system will also calculate calories and nutrition values. For obtaining better performance and accuracy in food recognition, system needs to extract multiple features.