Performance Evaluation of Exponential Discriminant Analysis with Feature Selection for Steganalysis

  • Gaurav Rajput Jawaharlal Nehru University, New Delhi
  • R.K. Agrawal Jawaharlal Nehru University, New Delhi
  • Namita Aggrawal Jawaharlal Nehru University, New Delhi
Keywords: Feature selection, Chernoff distance measure, kullback divergence, linear regression, steganalysis, exponential discriminant analysis

Abstract

The performance of supervised learning-based seganalysis depends on the choice of both classifier and features which represent the image. Features extracted from images may contain irrelevant and redundant features which makes them inefficient for machine learning. Relevant features not only decrease the processing time to train a classifier but also provide better generalisation. Linear discriminant classifier which is commonly used for classification may not be able to classify in better way non-linearly separable data. Recently, exponential discriminant analysis, a variant of linear discriminant analysis (LDA), is proposed which transforms the scatter matrices to a new space by distance diffusion mapping. This provides exponential discriminant analysis (EDA) much more discriminant power to classify non-linearly separable data and helps in improving classification accuracy in comparison to LDA. In this paper, the performance of EDA in conjunction with feature selection methods has been investigated. For feature selection, Kullback divergence, Chernoff distance measures and linear regression measures are used to determine relevant features from higher-order statistics of images. The performance is evaluated in terms classification error and computation time. Experimental results show that exponential discriminate analysis in conjunction with linear regression significantly performs better in terms of both classification error and compilation time of training classifier.

Defence Science Journal, 2012, 62(1), pp.19-24DOI:http://dx.doi.org/10.14429/dsj.62.1437

Author Biographies

Gaurav Rajput, Jawaharlal Nehru University, New Delhi
Mr Gaurav Rajput received MSc (Math) from Aligarh Muslim University and MTech from Jawaharlal Nehru University, New Delhi, Currently he is pursuing PhD from School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi. Her current area of research are Steganography and Steganalysis.
R.K. Agrawal, Jawaharlal Nehru University, New Delhi
Dr R.K. Agrawal obtained MTech (Computer Application) from Indian Institute of Technology Delhi, New Delhi and PhD (Computational Physics) from University of Delhi, Delhi. Presently, he is working as an Associate Professor at the School of
Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi. His current research areas are: Classification, feature extraction and selection for pattern recognition problems in domains of image processing, security, and bioinformatics.
Namita Aggrawal, Jawaharlal Nehru University, New Delhi
Ms Namita Aggarwal received BSc (Math) from University of Delhi, Delhi and MCA from Delhi University, Delhi. Currently she is pursuing PhD from School of Computer
and Systems Sciences, Jawaharlal Nehru University, New Delhi. Her current area of research is pattern recognition.
Published
2012-01-23
How to Cite
Rajput, G., Agrawal, R., & Aggrawal, N. (2012). Performance Evaluation of Exponential Discriminant Analysis with Feature Selection for Steganalysis. Defence Science Journal, 62(1), 19-24. https://doi.org/10.14429/dsj.62.1437
Section
Special Issue Papers