This paper is published in Volume-4, Issue-2, 2018
Area
Image Processing
Author
Gagandeep Singh, Sonika Jindal
Org/Univ
Shaheed Bhagat Singh State Technical Campus, Ferozepur, Punjab, India
Keywords
Multi-class Classification, Support Vector Machine, Feature Selection, Indoor Scene Recognition, SIFT
Citations
IEEE
Gagandeep Singh, Sonika Jindal. Adaptive M-SVM classification model qualified indoor scene images with hybrid feature selection approach, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Gagandeep Singh, Sonika Jindal (2018). Adaptive M-SVM classification model qualified indoor scene images with hybrid feature selection approach. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2) www.IJARIIT.com.
MLA
Gagandeep Singh, Sonika Jindal. "Adaptive M-SVM classification model qualified indoor scene images with hybrid feature selection approach." International Journal of Advance Research, Ideas and Innovations in Technology 4.2 (2018). www.IJARIIT.com.
Gagandeep Singh, Sonika Jindal. Adaptive M-SVM classification model qualified indoor scene images with hybrid feature selection approach, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Gagandeep Singh, Sonika Jindal (2018). Adaptive M-SVM classification model qualified indoor scene images with hybrid feature selection approach. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2) www.IJARIIT.com.
MLA
Gagandeep Singh, Sonika Jindal. "Adaptive M-SVM classification model qualified indoor scene images with hybrid feature selection approach." International Journal of Advance Research, Ideas and Innovations in Technology 4.2 (2018). www.IJARIIT.com.
Abstract
The results of the proposed model have been obtained in the form of the various performance parameters of statistical errors, precision, recall, F1-measure and overall accuracy. The proposed model has clearly outperformed the existing models in the terms of the overall accuracy. The proposed model improvement has been recorded higher than ten percent for all of the evaluated parameters against the existing models based upon SURF, FREAK, etc.
