Exploiting Contextual Information of Color Images through Feature Extraction Techniques for Semantic Segmentation
Cheruku Sandesh Kumar1, Vinod Kumar Sharma2, Rekha Chaturvedi3

1Dr. Cheruku Sandesh Kumar, Department of ECE, Amity University, Jaipur (Rajasthan), India.
2Mr. Vinod Kumar Sharma, Department of ECE, Amity University, Jaipur (Rajasthan), India.
3Rekha Chaturvedi, Department of CSE, Amity University, Jaipur (Rajasthan), India.
Manuscript received on 27 March 2019 | Revised Manuscript received on 08 April 2019 | Manuscript Published on 18 April 2019 | PP: 912-916 | Volume-7 Issue-6S March 2019 | Retrieval Number: F03860376S19/2019©BEIESP
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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: In our findings we try to explore spatial context to obtained good results of semantic segmentation. Spatial context has patch-to-patch and patch-to-background. Patch-to-patch context has semantic relationships on visual patterns of two stuffs of a image. Patch-to-background context had semantic relations in image patch and whole background region. In our research we have explored contextual relations based on CRF. CNNs pair-wise potential captures semantic correlation on nearby patches. Researchers in the past used CNN-Sparse CRF.In our model we used CNN-Dense CRF technique to refine our samples to sharpen the object boundaries. CNN-Dense CRF use pair-wise potentials for local smoothness of images. PairWise potentials are log-linear functions for semantic compatibility in image regions. CRF Pairwise is to develop coarse-level prediction. CRF and Potts-model-based pair-wise potential are jointed to obtained good results for semantic segmentation.
Keywords: Deep Learning, FCNN, ANN, Adaboost, CRF, SS-Semantic Segmentation.
Scope of the Article: Image Security