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An Image Segmentation Algorithm Based on Large-scale Randomized Tree Structure

Xiliang Zeng, Kuangfeng Ning


Using spectral clustering method in the image segmentation algorithm is difficult. To calculate the spectral weight matrix of the image is the actual problem. The weight matrix is defined by the distance of image pixels between the image points and the weight classes. The design of hierarchical image segmentation algorithm has used to be the algorithm in image segmentation. We can treat the image points by randomized classification sampling method. By adjusting the large-scaling factor, we can merge or split a large-scale class into smaller classes. The proposed method in the paper has large-scale features with randomness and robustness, and we called large-scale randomized tree image segmentation (LSRTIS). The experimental results in the paper are shown that LSRTIS method is effective and robust.


spectral clustering, large-scale randomized tree, image segmentation, LSRTIS.

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