基于核化粗糙集的聚类算法与萤火虫算法相融合的图像分割

Srujan Chinta
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引用次数: 4

摘要

近十年来,数据聚类方法被广泛应用于图像分割。在作者之前的一篇文章中,本文已经证明了将传统的聚类算法与萤火虫算法等元启发式算法相结合,可以提高输出的稳定性和收敛速度。众所周知,欧几里得距离作为一种度量相似性的方法存在一定的缺陷,因此本文将其替换为核函数来进行研究。实际上,作者将粗糙模糊C-Means (RFCM)和粗糙直觉模糊C-Means (RIFCM)与Firefly算法结合起来,用高斯基核或超切基核或径向基核取代欧几里德距离。本文将这些算法称为基于高斯核的粗糙模糊c -均值与萤火虫算法(GKRFCMFA)、基于超切线核的粗糙模糊c -均值与萤火虫算法(HKRFCMFA)、基于高斯核的粗糙直觉模糊c -均值与萤火虫算法(GKRIFCMFA)和基于超切线核的粗糙直觉模糊c -均值与萤火虫算法(HKRIFCMFA)。基于径向基核的粗糙模糊c -均值萤火虫算法(RBKRFCMFA)和基于径向基核的粗糙直觉模糊c -均值萤火虫算法(RBKRIFCMFA)。为了证明这些算法的性能优于相应的基于欧几里得距离的算法,本文使用了DB和Dunn指数等度量。输入数据包括三种不同类型的图像。此外,这个实验在不同数量的集群上也会有所不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kernelised Rough Sets Based Clustering Algorithms Fused With Firefly Algorithm for Image Segmentation
Data clustering methods have been used extensively for image segmentation in the past decade. In one of the author's previous works, this paper has established that combining the traditional clustering algorithms with a meta-heuristic like the Firefly Algorithm improves the stability of the output as well as the speed of convergence. It is well known now that the Euclidean distance as a measure of similarity has certain drawbacks and so in this paper we replace it with kernel functions for the study. In fact, the authors combined Rough Fuzzy C-Means (RFCM) and Rough Intuitionistic Fuzzy C-Means (RIFCM) with Firefly algorithm and replaced Euclidean distance with either Gaussian or Hyper-tangent or Radial basis Kernels. This paper terms these algorithms as Gaussian Kernel based rough Fuzzy C-Means with Firefly Algorithm (GKRFCMFA), Hyper-tangent Kernel based rough Fuzzy C-Means with Firefly Algorithm (HKRFCMFA), Gaussian Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (GKRIFCMFA) and Hyper-tangent Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (HKRIFCMFA), Radial Basis Kernel based rough Fuzzy C-Means with Firefly Algorithm (RBKRFCMFA) and Radial Basis Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (RBKRIFCMFA). In order to establish that these algorithms perform better than the corresponding Euclidean distance-based algorithms, this paper uses measures such as DB and Dunn indices. The input data comprises of three different types of images. Also, this experimentation varies over different number of clusters.
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