Clustering technique is used in image segmentation because of its simple and easy approach. However, the existing clustering techniques required prior information as input and the performance are entirely dependent on this prior information, which is the main drawback of the clustering approaches. Therefore, many researchers are trying to introduce a novel method with user free parameter. We proposed a clustering method, that is, independent of user parameters and later we used a region merging technique to improve the performance of the clustering output. In this article, we proposed a hybrid image segmentation method which is based on a clustering algorithm and black hole algorithm. In the clustering technique, we have used recursive density estimation technique of surrounding pixels. After clustering technique, presence of small segments may be present and it would give lower a performance of segmentation output. Therefore, a segment is merged with another segment by finding best matched segment. Black hole algorithm concept has been used to define the fitness of each segment and to find the best matching segment. We have compared the proposed method with the other clustering-based segmentation methods and different evaluation indices are used to calculate the performance, and the result proved the effectiveness of the proposed algorithm.

中文翻译：

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一种新的基于聚类和黑洞算法的混合图像分割方法

聚类技术由于其简单易行的方法而被用于图像分割。然而，现有的聚类技术需要先验信息作为输入，性能完全依赖于先验信息，这是聚类方法的主要缺点。因此，许多研究人员正在尝试引入一种具有用户自由参数的新方法。我们提出了一种聚类方法，即独立于用户参数，后来我们使用区域合并技术来提高聚类输出的性能。在本文中，我们提出了一种基于聚类算法和黑洞算法的混合图像分割方法。在聚类技术中，我们使用了周围像素的递归密度估计技术。聚类技术后，可能存在小片段，这会降低分割输出的性能。因此，通过找到最匹配的段，将一个段与另一个段合并。黑洞算法概念已被用于定义每个片段的适应度并找到最佳匹配片段。我们将所提出的方法与其他基于聚类的分割方法进行了比较，并使用不同的评价指标来计算性能，结果证明了所提出算法的有效性。