Abstract
Image classification is a challenging task in image processing especially in the case of blurry and noisy images. In this work, we present an extension of scene oriented hierarchical classification of blurry and noisy images using Support Vector Machine (SVM) and Fuzzy C-Mean. Generally, a system for scene-oriented classification of blurry and noisy images attempts to simulate major features of the human visual observation. These approaches are based on three strategies such as Global pathway for extracting essential signature of image, local pathway for extracting local features, and then outcome of both global and local phase are combined and define feature vector and clustered using Monte Carlo approach. Afterwards, these clustered results are fed to a SOTA Algorithm (combination of self organizing map and hierarchical clustering) for final classification. But in these approaches, combination of self organizing map and hierarchical clustering has the problem in terms of accuracy and computation time of classification, especially when used large dataset for classification. To overcome this problem, we propose a combination of Support Vector Machine (SVM) and Fuzzy C-mean. Our proposed approach provides better result in terms of accuracy, especially when used with large dataset. The proposed method is computationally efficient because fuzzy c-mean clustering is faster and less time consuming as compared to hierarchical clustering.
Highlights
Classification is an information processing task in which images are categorized into several groups
Proposed technique is implemented for image classification of blurry and noisy images
We have proposed a novel image classification method
Summary
Classification is an information processing task in which images are categorized into several groups. In real world thousands of images are generated daily, which implies that the requirements to classify for access them by an easy and faster way. Several methods have been proposed by researchers in the literature to classify images and they provide good classification result but they fail to provide satisfactory classification result, especially when the image contains blurry and noisy content. The main classification difficulty is raised due to the visual ambiguity generated due to noisy content. Even though a lot of research has been directed to deal with classification of visual information, most of the techniques only address the fundamental problem of classification. That means only classify the normal images and not address the classification problems of blurry and noisy images
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