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.
Highlights
Massive expansion of image data has been observed due to the use of digital cameras, Internet, and other image capturing devices in recent times
Percentagewise comparison of classification results with K-Nearest Neighbor (KNN) classifier for misclassification rate (MR) and F1 score has been given in Tables 1 and 2 for different numbers of ordered mean values as feature vectors
The minimum misclassification rate (MR) of 6.4% and highest F1 score of 70.9% were observed with eight-ordered mean values as feature vectors computed from eight descending order subdivisions of the ordered one-dimensional array
Summary
Massive expansion of image data has been observed due to the use of digital cameras, Internet, and other image capturing devices in recent times. A novel technique for feature extraction using values of ordered means has been proposed in this work. Discrete sine transform and Kekre transform were applied on the images to extract partial coefficients as feature vectors in transform domain. The two transform domain techniques were compared for superior classification results and discrete sine transform (DST) was chosen over Kekre transform for fusion with the ordered mean feature extraction process for better classification results. It was evaluated for classification performance and was compared to existing widely used techniques for feature extraction. The results have clearly indicated superior performance of classification with multiview method of feature extraction with proposed technique over the existing techniques
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