A Sparse Representation Based Classification Algorithm for Chinese Food Recognition

Obesity is becoming a widely concerned health problem of most part of the world. Computer vision based recognition system has great potential to be an efficient tool to monitor food intake and cope with the growing problem of obesity. This paper proposes a food recognition algorithm based on sparse representation. The proposed algorithm learns overcomplete dictionaries from local descriptors including texture and color features that are extracted from food image patches. With the learned two overcomplete dictionaries, a feature vector of the food image can be generated with the sparsely encoded local descriptors. SVM is used for the classification. This research creates a Chinese food image dataset for experiments. Classifying Chinese food is more challenging because they are not as distinguishable visually as western food. The proposed algorithm achieves an average classification accuracy of 97.91% in a dataset of 5309 images that comprises 18 classes. The proposed method can be easily employed to dataset with more classes. Our results demonstrate the feasibility of using the proposed algorithm for food recognition.