Acta Optica Sinica, Volume. 29, Issue 10, 2762(2009)
Multiscale Image Segmentation Based on Graph Weighted Kernel K-means
An improved minimum cut model is presented considering that the minimum cut criteria favors cutting small sets of isolated nodes,then equivalence relation between the improved minimum cut model and weighted kernel k-means is researched,and the influence of different similarity functions on the results of segmentation are also analysed. And based on these,a multiscale image segmentation method based on graph weighted kernel k-means is proposed,this method avoids calculating graph spectral,which is a key step when using graph cut model to segment images,also,it avoids selecting kernel matrix,which is important to the weighted kernel k-means,finally it realizes multiscale image segmentation. The analysis of anti-noise and experimental results on a number of optical images show the effectiveness of this method.
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Li Yuchuan, Tian Zheng. Multiscale Image Segmentation Based on Graph Weighted Kernel K-means[J]. Acta Optica Sinica, 2009, 29(10): 2762
Category: Image Processing
Received: Mar. 20, 2009
Accepted: --
Published Online: Oct. 19, 2009
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