Towards a Pervasive Cloud Computing Based Food Image Recognition

Food image recognition is challenging due to the diversity of food, and color, light, view angles' effect on food image. The recognition process is also a computation heavy process. Therefore, We also propose to use pervasive cloud computing paradigm to improve the performance of food image recognition. Based on empirical and experimental explorations, we propose to use SIFT(Scale Invariant Feature Transform) and Gabor descriptors as food image features and KMeans algorithm for feature clustering. Evaluations show that the proposed approach can give acceptable recognition rate with good performance gains.