Classification of Single-Food Images by Combining Local HSV-AKAZE Features and Global Features

This paper presents a system for assisting nutrition management for solitary elderly persons. Since dealing with diseases is one of the important issues for solitary elderly, their health control in daily life has been in focus in recent years. As preprocessing to develop a nutrition management system for solitary elderly, systems for discriminating the category of a food image have been proposed. However, classification of food images is still a challenging task due to the variety of their shape and color. In order to improve the performance on the classification, we propose three regions of interests extracted by HSV-AKAZE. The three regions are used to extract various local features such as AKAZE, HSV-AKAZE, and color information, enhances the classification performance. Evaluation experiments for 2000 food images in 50 categories have shown that the classification accuracy has increased by 8% compared with the existing system. Keywords— HSV-AKAZE, Machine Learning, Solitary Elderly Persons, ROI, Single Food Image