Table of Contents
Advances in Computer Engineering
Volume 2014, Article ID 454876, 15 pages
http://dx.doi.org/10.1155/2014/454876
Research Article

Feature Extraction with Ordered Mean Values for Content Based Image Classification

1Pimpri Chinchwad College of Engineering, Akurdi, Sec. 26, Pradhikaran, Nigdi, Pune, Maharashtra 411033, India
2Xavier Institute of Social Service, Dr. Camil Bulcke Path (Purulia Road), P.O. Box 7, Ranchi, Jharkhand 834001, India
3A. K. Choudhury School of Information Technology, University of Calcutta, 92 APC Road, Kolkata, West Bengal 700009, India

Received 24 July 2014; Revised 18 November 2014; Accepted 18 November 2014; Published 17 December 2014

Academic Editor: Lijie Li

Copyright © 2014 Sudeep Thepade et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Categorization of images into meaningful classes by efficient extraction of feature vectors from image datasets has been dependent on feature selection techniques. Traditionally, feature vector extraction has been carried out using different methods of image binarization done with selection of global, local, or mean threshold. This paper has proposed a novel technique for feature extraction based on ordered mean values. The proposed technique was combined with feature extraction using discrete sine transform (DST) for better classification results using multitechnique fusion. The novel methodology was compared to the traditional techniques used for feature extraction for content based image classification. Three benchmark datasets, namely, Wang dataset, Oliva and Torralba (OT-Scene) dataset, and Caltech dataset, were used for evaluation purpose. Performance measure after evaluation has evidently revealed the superiority of the proposed fusion technique with ordered mean values and discrete sine transform over the popular approaches of single view feature extraction methodologies for classification.