Abstract
In this paper, we propose a new Rnk segmentation algorithm based on N-cut criterion, which is more reasonable than common segmentation criteria. The algorithm can improve the existing segmentation by changing the vertex pairs of N-cut values continuously, and finally get the optimal segmentation. A hashing technique is proposed to improve the efficiency of finding the optimal exchange vertex pairs. When the graph is dense matrix, the improvement effect is especially obvious. The experimental results of random graph segmentation show that the proposed algorithm is more reasonable and faster than the traditional KL algorithm.
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Special Funding Project of China Postdoctoral Science Foundation (2014T70967); Natural Science Research Key Project of Anhui Province Higher School (KJ2017A630); Quality Engineering Project of Anhui Provincial (2016jxtd055); Key Construction Discipline Project at College Level of Anhui Xinhua University (zdxk201702); Institute Project at College Level of Anhui Xinhua University (yjs201706).
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Xie, X., Zhang, F., Wang, X., Zeng, Y., Li, C. (2019). Rnk New Algorithm Based on N-Cut Graph Segmentation. In: Deng, K., Yu, Z., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2018. Advances in Intelligent Systems and Computing, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-00214-5_8
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DOI: https://doi.org/10.1007/978-3-030-00214-5_8
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