ProgramsView all programs
From the news
- Chancellor Amma Addresses the Parliament of World’s Religions
- Amrita Students Qualify for the European Mars Rover Challenge
Publication Type : Conference Paper
Publisher : 2017 IEEE International Conference on Signal and Image Processing Applications
Source : 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), IEEE, Kuching, Sarawak; Malaysia, p.12-14 (2017)
Url : https://www.scopus.com/record/display.uri?eid=2-s2.0-85041422926&origin=resultslist&sort=plf-f&src=s&sid=18140dd158b59fc89f538e9f430579a7&sot=autdocs&sdt=autdocs&sl=18&s=AU-ID%2836096164300%29&relpos=41&citeCnt=3&searchTerm=
Keywords : Adaptation models, adapted Gaussian mixture model, AGMM-DBN-SVM classification models, bags of visual words, belief networks, color-to-grayscale image mapping technique, Computer architecture, Deep belief networks, Edge detection, Feature extraction, Gaussian processes, image classification, Image coding, Image color analysis, image colour analysis, image decolorization, image representation, Image segmentation, improved color scene classification system, Learning systems, Neurons, Oliva Torralba scene dataset, rgb2gray, scale-invariant feature transform, Singular value decomposition, Support vector machines, Transforms, Video signal processing
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2017
Abstract : In general, the three main modules of the color scene classification systems are image decolorization, feature extraction and classification. The work presented in this paper focuses on image decolorization and classification as two stages. The first stage or objective of this paper is to improve the performance of the color scene classification system using deep belief networks (DBN) and support vector machines (SVM). Therefore, color scene classification system termed as AGMM-DBN-SVM is proposed using the existing feature extraction technique called bags of visual words (BoW) derived from the dense scale-invariant feature transform (SIFT) and adapted gaussian mixture models (AGMM). The second stage of the presented work is to combine the proposed AGMM-DBN-SVM classification models obtained for the two different image decolorization methods called rgb2gray and singular value decomposition (SVD) based color-to-grayscale image mapping techniques to significantly increase the performance of the proposed color scene classification system. The effectiveness of the proposed framework is experimented on Oliva Torralba (OT) scene dataset containing 8 different classes. The classification rate of the proposed color scene classification system applied on OT 8 scene dataset is significantly greater than the one of the existing benchmarks color scene classification system developed using AGMM and SVM.
Cite this Research Publication : Sowmya, Ajay, A., Dr. Govind D., and Dr. Soman K. P., “Improved color scene classification system using deep belief networks and support vector machines”, in 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuching, Sarawak; Malaysia, 2017, pp. 12-14 .