Food Image Recognition Using Pervasive Cloud Computing

Food image recognition is increasingly important for e-health applications. But this is a challenging topic due to the diversity of food, and color, light, view angles' effect on food image. 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. We also propose to use pervasive cloud computing paradigm to improve the performance of food image recognition due to the heavy computing requirement for large number of concurrent recognition requests. Evaluations show that the proposed approach can give acceptable recognition rate, and MapReduce programming can provide promising performance advantage compared to traditional client server approach.