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Feature Extraction with Ordered Mean Values for Content Based Image Classification

DOI: 10.1155/2014/454876

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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. 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 efficient 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 Thepade in [4]. Conventional approaches for feature extraction from images have considered binarization as a means to differentiate the image into higher and lower intensity values as adopted in one of their approaches by Kekre and Thepade 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 coefficient 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 coefficients as feature vectors in

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