Image pixel clustering or segmentation intends to identify pixel groups on an image without any preliminary labels. It remains a challenging task in computer vision since the size and shape of object segments are varied. Moreover, determining the segment number in an image without prior knowledge of the image content is an NP-hard problem. In this paper, we present an automatic image pixel clustering scheme based on mussels wandering optimization. An activation variable is applied to determine the number of clusters automatically with the cluster centers optimization. We revise the within- and between-class sum of squares ratio for random natural image content and develop a novel fitness function for the image pixel clustering task. Our proposed scheme is compared against existing state-of-the-art techniques using both synthetic data and real ASD dataset. Experimental results show the superiority performance of the proposed scheme.

中文翻译：

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基于贻贝游走优化的图像像素自动聚类

图像像素聚类或分割旨在识别图像上没有任何初步标签的像素组。由于对象片段的大小和形状各不相同，因此它在计算机视觉中仍然是一项具有挑战性的任务。此外，在没有图像内容先验知识的情况下确定图像中的段数是一个 NP-hard 问题。在本文中，我们提出了一种基于贻贝漂移优化的自动图像像素聚类方案。应用激活变量来通过聚类中心优化自动确定聚类的数量。我们修改了随机自然图像内容的类内和类间平方和比，并为图像像素聚类任务开发了一种新的适应度函数。我们提出的方案与使用合成数据和真实 ASD 数据集的现有最先进技术进行了比较。实验结果表明了所提方案的优越性能。