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Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 8508294, 14 pages
Research Article

A Fast Color Image Segmentation Approach Using GDF with Improved Region-Level Ncut

1College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
2School of Software, Beijing Institute of Technology, Beijing 100081, China

Correspondence should be addressed to Shuliang Wang

Received 4 August 2017; Revised 17 November 2017; Accepted 29 November 2017; Published 2 January 2018

Academic Editor: Marco Mussetta

Copyright © 2018 Ying Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Color image segmentation is fundamental in image processing and computer vision. A novel approach, GDF-Ncut, is proposed to segment color images by integrating generalized data field (GDF) and improved normalized cuts (Ncut). To start with, the hierarchy-grid structure is constructed in the color feature space of an image in an attempt to reduce the time complexity but preserve the quality of image segmentation. Then a fast hierarchy-grid clustering is performed under GDF potential estimation and therefore image pixels are merged into disjoint oversegmented but meaningful initial regions. Finally, these regions are presented as a weighted undirected graph, upon which Ncut algorithm merges homogenous initial regions to achieve final image segmentation. The use of the fast clustering improves the effectiveness of Ncut because regions-based graph is constructed instead of pixel-based graph. Meanwhile, during the processes of Ncut matrix computation, oversegmented regions are grouped into homogeneous parts for greatly ameliorating the intermediate problems from GDF and accordingly decreasing the sensitivity to noise. Experimental results on a variety of color images demonstrate that the proposed method significantly reduces the time complexity while partitioning image into meaningful and physically connected regions. The method is potentially beneficial to serve object extraction and pattern recognition.