一种新的基于超像素聚类的图像分割算法  

A New Image Segmentation Algorithm Based on SuperPixel Clustering

作　　者：姜全春 王宁[1] 李雷[1] 蒋林华[1] JIANG Quan-chun;WANG Ning;LI Lei;JIANG Lin-hua(University of Shanghai for Science and Technology, Shanghai, China, 200093)

机构地区：[1]上海理工大学

出　　处：《软件》2019年第6期44-48,共5页Software

基　　金：国家自然科学基金项目(批准号:61775139,61332009)

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.

关 键 词：密度聚类 图像分割 超像素分割 局部邻域 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]