IEEE Trans Image Process. 2016 Jul;25(7):3182-3193. doi: 10.1109/TIP.2016.2559803.
Clustering image pixels is an important image segmentation technique. While a large amount of clustering algorithms have been published and some of them generate impressive clustering results, their performance often depends heavily on user-specified parameters. This may be a problem in the practical tasks of data clustering and image segmentation. In order to remove the dependence of clustering results on user-specified parameters, we investigate the characteristics of existing clustering algorithms and present a parameter-free algorithm based on the DSets (dominant sets) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms. First, we apply histogram equalization to the pairwise similarity matrix of input data and make DSets clustering results independent of user-specified parameters. Then, we extend the clusters from DSets with DBSCAN, where the input parameters are determined based on the clusters from DSets automatically. By merging the merits of DSets and DBSCAN, our algorithm is able to generate the clusters of arbitrary shapes without any parameter input. In both the data clustering and image segmentation experiments, our parameter-free algorithm performs better than or comparably with other algorithms with careful parameter tuning.
聚类图像像素是一种重要的图像分割技术。虽然已经发布了大量的聚类算法，其中一些算法生成了令人印象深刻的聚类结果，但它们的性能往往严重依赖于用户指定的参数。这在数据聚类和图像分割的实际任务中可能是一个问题。为了消除聚类结果对用户指定参数的依赖，我们研究了现有聚类算法的特点，并提出了一种基于 DSets（支配集）和 DBSCAN（基于密度的带有噪声的应用空间聚类）算法的无参数算法。首先，我们对输入数据的成对相似性矩阵应用直方图均衡化，使 DSets 聚类结果不依赖于用户指定的参数。然后，我们使用 DBSCAN 从 DSets 中扩展聚类，其中输入参数是根据 DSets 中的聚类自动确定的。通过合并 DSets 和 DBSCAN 的优点，我们的算法能够生成任意形状的聚类，而无需任何参数输入。在数据聚类和图像分割实验中，我们的无参数算法在经过仔细参数调整后，性能优于或可与其他算法媲美。
