Publication Type : Conference Proceedings
Publisher : Springer International Publishing AG 2018
Source : Advances in Intelligent Systems and Computing, International Symposium on Signal Processing and Intelligent Recognition Systems SIRS 2017, Springer International Publishing AG 2018, Volume 678, Manipal, India, p.167-177 (2018)
Url : https://www.scopus.com/record/display.uri?eid=2-s2.0-85030174917&origin=resultslist&sort=plf-f&src=s&sid=18140dd158b59fc89f538e9f430579a7&sot=autdocs&sdt=autdocs&sl=18&s=AU-ID%2836096164300%29&relpos=30&citeCnt=7&searchTerm=
Keywords : Classifiers, Color, Color image processing, Decision trees, image analysis, Image processing, Neural networks, Signal processing, Singular value decomposition
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2018
Abstract : Scene classification systems have become an integral part of computer vision. Recent developments have seen the use of deep scene networks based on convolutional neural networks (CNN), trained using millions of images to classify scenes into various categories. This paper proposes the use of one such pre-trained network to classify specific scene categories. The pre-trained network is combined with the simple classifiers namely, random forest and extra tree classifiers to classify scenes into 8 different scene categories. Also, the effect of different color spaces such as RGB, YCbCr, CIEL*a*b* and HSV on the performance of the proposed CNN based scene classification system is analyzed based on the classification accuracy. In addition to this, various intensity planes extracted from the said color spaces coupled with color-to-gray image conversion techniques such as weighted average, and singular value decomposition (SVD) are also taken into consideration and their effects on the performance of the proposed CNN based scene classification system are also analyzed based on the classification accuracy. The experiments are conducted on the standard Oliva Torralba (OT) scene data set which comprises of 8 classes. The analysis of classification accuracy obtained for the experiments conducted on OT scene data shows that the different color spaces and the intensity planes extracted from various color spaces and color-to-gray image conversion techniques do affect the performance of proposed CNN based scene classification system.
Cite this Research Publication : Dr. Govind D., Sowmya, Sachin, R., and Dr. Soman K. P., “Dependency of Various Color and Intensity Planes on CNN Based Image Classification”, Advances in Intelligent Systems and Computing, International Symposium on Signal Processing and Intelligent Recognition Systems SIRS 2017, vol. 678. Springer International Publishing AG 2018, Manipal, India, pp. 167-177, 2018.