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A New Connected Coherence Tree Algorithm For Image Segmentation
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A New Connected Coherence Tree Algorithm For Image Segmentation

Vol. 6, No. 4, April 24, 2012
10.3837/tiis.2012.04.014, Download Paper (Free):

Abstract

In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.


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Cite this article

[IEEE Style]
J. Zhou, S. Gao and Z. Jin, "A New Connected Coherence Tree Algorithm For Image Segmentation," KSII Transactions on Internet and Information Systems, vol. 6, no. 4, pp. 1188-1202, 2012. DOI: 10.3837/tiis.2012.04.014.

[ACM Style]
Jingbo Zhou, Shangbing Gao, and Zhong Jin. 2012. A New Connected Coherence Tree Algorithm For Image Segmentation. KSII Transactions on Internet and Information Systems, 6, 4, (2012), 1188-1202. DOI: 10.3837/tiis.2012.04.014.