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An Efficient Image Segmentation Method Based on Linear Discriminant Analysis and K-means Algorithm with Automatically Splitting and Merging Clusters
Image segmentation plays a significant role in image processing, pattern recognition and as well as in computer vision. It aims to classify the meaningful objects residing in the image. This paper presents an efficient image segmentation method in which the linear discriminant analysis (LDA) is used for initial segmentation of the image through unsupervised manner. After the initial segmentation, the segmented image is used by the K-means algorithm for further segmentation. Finally, the procedures of automatically splitting and merging of clusters are applied to obtain the better segmented image. For analyzing the performance, we calculate the Davies-Bouldin index (DB-index) measure. The observation shows that, this method gives the better results compared with K-means algorithm, self organizing feature map (SOFM) and LDA based image segmentation method and also LDA and K-means algorithm based image segmentation method for a set of natural images.
Self Organizing Feature Map, Linear Discriminant Analysis, K-means, Automatically splitting and merging, DB-index measure.
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