IMAGE SEGMENTATION AND RECOGNITION FOR MULTI-CLASS CHINESE FOOD

For multi-class food images, an excellent segmentation method has a great influence on accuracy of recognition result, and hence improve the effectiveness of dietary management for diabetics. For Chinese food images, there are some challenges during the processing, such as blurred outlines, rich colors, and varied appearances due to various cooking methods. To overcome these difficulties, we propose a ChineseFoodSeg approach to obtain accurate and efficient multi-class segmentation. A food recognition model, Two-Path Global Local Network (TPGLNet), is also introduced to jointly learn complementary global and local features of the bounding box and the segment. Experiments on the ChineseDiabetesFood187 dataset collected by us demonstrate that the new methods are competitive compared to existing segmentation and recognition methods.