一种新的基于超像素聚类的图像分割算法
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摘要
本文提出了一种使用具有噪声的基于密度的聚类方法进行超像素聚类来提高图像分割准确性的方法,首先以较低计算成本得到超像素分割,然后我们再利用密度聚类的原理将相关联的超像素聚集到一起,利用超像素对图像边缘信息的准确分割,来提高图像分割的准确性。我们在构建图形时使用局部邻域将算法应用于分割中,并利用DBSCAN既可以适用于凸样本集,也可以适用于非凸样本集的特性对超像素进行聚类分析。将所有各组紧密相连的样本划为各个不同的类别,则我们就得到了最终的所有聚类类别结果。该方法的一个重要特征是其能够在像素点密度大过某个阈值时,保留图像区域中的细节。
This paper proposes a method of using super-pixel clustering based on the density-based clustering method to improve the accuracy of image segmentation. Firstly, the super-pixel segmentation is obtained at a lower computational cost, and then we use the principle of density clustering. The associated superpixels are gathered together. and uses superpixels to accurately segment the edge information of the image to improve the accuracy of image segmentation. We use the local neighborhood to apply the algorithm to the segmentation when constructing the graph, and use DBSCAN to apply to both the convex sample set and the non-convex sample set to cluster the superpixel. By grouping all the closely connected samples into different categories, we get the final results for all cluster categories. An important feature of this method is its ability to preserve details in the image area when the pixel density is greater than a certain threshold.
This paper proposes a method of using super-pixel clustering based on the density-based clustering method to improve the accuracy of image segmentation. Firstly, the super-pixel segmentation is obtained at a lower computational cost, and then we use the principle of density clustering. The associated superpixels are gathered together. and uses superpixels to accurately segment the edge information of the image to improve the accuracy of image segmentation. We use the local neighborhood to apply the algorithm to the segmentation when constructing the graph, and use DBSCAN to apply to both the convex sample set and the non-convex sample set to cluster the superpixel. By grouping all the closely connected samples into different categories, we get the final results for all cluster categories. An important feature of this method is its ability to preserve details in the image area when the pixel density is greater than a certain threshold.
引文
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[13]贾耕云,赵海英,刘菲朵.基于超像素的Graph-Based图像分割算法[J].北京邮电大学学报,2018,41(3):46-50.
[14]Vendramin,L.,Campello,R.J.,Hruschka,E.R.Relative clustering validity criteria:a comparative overview.Stat.Anal.Data Min,2010,3(4):209-235.
[2]丁亮,张永平,张雪英.图像分割方法及性能评价综述[J].软件,2010,12:78-83.
[3]孙吉贵,刘杰,赵连宇.聚类算法研究[J].软件学报,2008,19(1):48-61.
[4]郑金志,郑金敏,汪玉琳.基于优化初始聚类中心的改进WFCM图像分割算法[J].软件,2015,04:136-142.
[5]Mihael Ankerst,Markus M.Breunig,Hans-Peter Kriegel,and J?rg Sander.1999.OPTICS:ordering points to identify the clustering structure[J].In Proceedings of the 1999 ACMSIGMOD international conference on Management of data(SIGMOD'99),1999,28(2):49-60.
[6]Felzenszwalb P F,Huttenlocher D P.Efficient Graph-Based Image Segmentation[J].International Journal of Computer Vision,2004,59(2):167-181.
[7]Achanta R,Shaji A,Smith K,Lucchi A,Fua P,Süsstrunk S.SLIC superpixels compared to state-of-the-art superpixel methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282.
[8]Liu M Y,Tuzel O,Ramalingam S,Chellappa R.Entropy-rate clustering:cluster analysis via maximizing a submodular function subject to a matroid constraint[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(1):99-112.
[9]Zhu Song,Cao Danhua,Wu Yubin,Jiang Shixiong.Improved accuracy of superpixel segmentation by region merging method[J].Frontiers of Optoelectronics,2016,9(4):633-639.
[10]Wu Z,Leahy R M.An Optimal Graph Theoretic Approach to Data Clustering:Theory and Its Application to Image Segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,15(11):1101-1113.
[11]Perona P,Freeman W.A factorization approach to grouping[C].European Conference on Computer Vision.Springer,Berlin,Heidelberg,1998.
[12]Liu F,Gleicher M.Region Enhanced Scale-Invariant Saliency Detection[C].Proceedings of the 2006 IEEE International Conference on Multimedia and Expo,ICME 2006,July 9-122006,Toronto,Ontario,Canada.IEEE,2006.
[13]贾耕云,赵海英,刘菲朵.基于超像素的Graph-Based图像分割算法[J].北京邮电大学学报,2018,41(3):46-50.
[14]Vendramin,L.,Campello,R.J.,Hruschka,E.R.Relative clustering validity criteria:a comparative overview.Stat.Anal.Data Min,2010,3(4):209-235.