Elsevier

Procedia Computer Science

Volume 55, 2015, Pages 1015-1022
Procedia Computer Science

Image Segmentation via Improving Clustering Algorithms with Density and Distance

https://doi.org/10.1016/j.procs.2015.07.096Get rights and content
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Abstract

Image segmentation problem is a fundamental task and process in computer vision and image processing applications. It is well known that the performance of image segmentation is mainly influenced by two factors: the segmentation approaches and the feature presentation. As for image segmentation methods, clustering algorithm is one of the most popular approaches. However, most current clustering-based segmentation methods exist some problems, such as the number of regions of image have to be given prior, the different initial cluster centers will produce different segmentation results and so on. In this paper, we present a novel image segmentation approach based on DP clustering algorithm. Compared with the current methods, our method has several improved advantages as follows: 1) This algorithm could directly give the cluster number of the image based on the decision graph; 2) The cluster centers could be identified correctly; 3) We could simply achieve the hierarchical segmentation according to the applications requirement. A lot of experiments demonstrate the validity of this novel segmentation algorithm.

Keywords

Image Segmentation
Clustering Algorithm
Feature Representation ;

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Peer-review under responsibility of the Organizing Committee of ITQM 2015.