Fine Grained Food Image Segmentation through EA-DCNNs

-The recognition of Indian food can be considered as a fine-grained visual recognition due to the same class photos may provide considerable amount of variability. Thus, an effective segmentation and classification method is needed to provide refined analysis. While only consideration of CNN may cause limitation through the absence of constraints such as shape and edge that causes output of segmentation to be rough on their edges. In order overcome this difficulty, a post-processing step is required; in this paper we proposed an EA based DCNNs model for effective segmentation. The EA is directly formulated with the DCNNs approach, which allows training step to get beneficial from both the approaches for spatial data relationship. The EA will help to get better-refined output after receiving the features from powerful DCNNs. The EA-DCNN training model contains convolution, rectified linear unit and pooling that is much relevant and practical to get optimize segmentation of food image. In order to evaluate the performance of our proposed model we will compare with the ground-truth data at several validation parameters