Research Article Feature Extraction with Ordered Mean Values for Content Based Image Classification Sudeep Thepade, 1 Rik Das, 2 and Saurav Ghosh 3 1 Pimpri Chinchwad College of Engineering, Akurdi, Sec. 26, Pradhikaran, Nigdi, Pune, Maharashtra 411033, India 2 Xavier Institute of Social Service, Dr. Camil Bulcke Path (Purulia Road), P.O. Box 7, Ranchi, Jharkhand 834001, India 3 A. K. Choudhury School of Information Technology, University of Calcutta, 92 APC Road, Kolkata, West Bengal 700009, India Correspondence should be addressed to Rik Das; rikdas78@gmail.com Received 24 July 2014; Revised 18 November 2014; Accepted 18 November 2014; Published 17 December 2014 Academic Editor: Lijie Li Copyright © 2014 Sudeep Tepade et al. Tis 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. Categorization of images into meaningful classes by efcient extraction of feature vectors from image datasets has been dependent on feature selection techniques. Traditionally, feature vector extraction has been carried out using diferent methods of image binarization done with selection of global, local, or mean threshold. Tis paper has proposed a novel technique for feature extraction based on ordered mean values. Te proposed technique was combined with feature extraction using discrete sine transform (DST) for better classifcation results using multitechnique fusion. Te novel methodology was compared to the traditional techniques used for feature extraction for content based image classifcation. Tree benchmark datasets, namely, Wang dataset, Oliva and Torralba (OT-Scene) dataset, and Caltech dataset, were used for evaluation purpose. Performance measure afer 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 classifcation. 1. Introduction Massive expansion of image data has been observed due to the use of digital cameras, Internet, and other image capturing devices in recent times. Classifying images has been considered as a vital research domain for efcient handling of image data as discussed by Lu and Weng in [1]. Recognition of images based on the content has been dependent on extraction of visual features from the dataset as suggested by Liu and Bai in [2], Agrawal et al. in [3], and Kekre and Tepade in [4]. Conventional approaches for feature extraction from images have considered binarization as a means to diferentiate the image into higher and lower intensity values as adopted in one of their approaches by Kekre and Tepade in [5] and Shaikh et al. in [6], respectively. Multiple applications of binarization on graphic images and document images have been implemented, some of which were proposed by Ntirogiannis et al. [7], Sezgin and Sankur [8], and Yang and Yan [9]. A novel technique for feature extraction using values of ordered means has been proposed in this work. However, an image encompassed diverse features which can hardly be described with a single technique of feature extraction. Image recognition has been stimulated in the past by feature extraction with partial coefcient in transform domain as discussed by Kekre et al. [10]. Hence discrete sine transform and Kekre transform were applied on the images to extract partial coefcients as feature vectors in transform domain. Te two transform domain techniques were compared for superior classifcation results and discrete sine transform (DST) was chosen over Kekre transform for fusion with the ordered mean feature extraction process for better classifcation results. It was evaluated for classifcation performance and was compared to existing widely used techniques for feature extraction. Te results have clearly indicated superior performance of classifcation with multiview method of feature extraction with proposed technique over the existing techniques. 2. Related Work Selection of threshold has been an important criterion for feature extraction with binarization. Treshold selection has Hindawi Publishing Corporation Advances in Computer Engineering Volume 2014, Article ID 454876, 15 pages http://dx.doi.org/10.1155/2014/454876