Fine Grained Food Image Segmentation through EA-DCNNs
1Vishwanath.C.Burkapalli*, Dept. Of Information Science and Engineering, PDA College of Engineering, Kalaburgi, Karnataka, India.
2Priyadarshini.C.Patil, Dept. Of Computer Science and Engineering, PDA College of Engineering, Kalaburgi, Karnataka, India.
Manuscript received on October 13, 2019. | Revised Manuscript received on 21 October, 2019. | Manuscript published on November 10, 2019. | PP: 212-218 | Volume-9 Issue-1, November 2019. | Retrieval Number: A3982119119/2019©BEIESP | DOI: 10.35940/ijitee.A3982.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Deep Convolutional Neural Networks (DCNNs); Rectified Linear Unit (ReLu); Edge Adaptive (EA); Pooling; Convolution.
Scope of the Article: Neural Information Processing