Normalized tree partitioning for image segmentation

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...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Jingdong Wang, Yangqing Jia, Xian-Sheng Hua, Changshui Zhang, Long Quan
• Keywords: Computer Vision, CVPR 2008, K-way Tree Partitioning, Tree Partitioning, Tree Partitioning Method