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2008 IEEE Conference on Computer Vision and Pattern Recognition (2008)
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2242-5
pp: 1-8
Jingdong Wang , Microsoft Research Asia, Beijing, China
Yangqing Jia , Tsinghua University, Beijing, China
Xian-Sheng Hua , Microsoft Research Asia, Beijing, China
Changshui Zhang , Tsinghua University, Beijing, China
Long Quan , Hong Kong University of Science and Technology, China
ABSTRACT
In this paper, we propose a novel graph based clustering approach with satisfactory clustering performance and low computational cost. It consists of two main steps: tree fitting and partitioning. We first introduce a probabilistic method to fit a tree to a data graph under the sense of minimum entropy. Then, we propose a novel tree partitioning method under a normalized cut criterion, called Normalized Tree Partitioning (NTP), in which a fast combinatorial algorithm is designed for exact bipartitioning. Moreover, we extend it to k-way tree partitioning by proposing an efficient best-first recursive bipartitioning scheme. Compared with spectral clustering, NTP produces the exact global optimal bipartition, introduces fewer approximations for k-way partitioning and can intrinsically produce superior performance. Compared with bottom-up aggregation methods, NTP adopts a global criterion and hence performs better. Last, experimental results on image segmentation demonstrate that our approach is more powerful compared with existing graph-based approaches.
INDEX TERMS
CITATION

Jingdong Wang, Changshui Zhang, Yangqing Jia, Xian-Sheng Hua and Long Quan, "Normalized tree partitioning for image segmentation," 2008 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Anchorage, AK, USA, 2008, pp. 1-8.
doi:10.1109/CVPR.2008.4587454
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