文章基本信息

标题：Feature Extraction with Ordered Mean Values for Content Based Image Classification
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作者：Sudeep Thepade ; Rik Das ; Saurav Ghosh 等
期刊名称：Advances in Computer Engineering
印刷版ISSN：2356-6620
出版年度：2014
卷号：2014
DOI：10.1155/2014/454876
出版社：Hindawi Publishing Corporation
摘要：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.