The advances in the image processing area demand for improvement in image segmentation methods. Effect of light and noise being ignored in image segmentation while tracing the objects of interest in addition to this texture is also one of the most important factors for analyzing an image automatically. Among the diverse segmentation methods, graph-based techniques are widespread because of their capabilities of generating accurate segmentation structures. In this paper, we have proposed a novel technique by using discrete particle swarm optimization and multilevel partitioning for segmentation of an image. The developed technique has lesser complexity, better efficiency and gives improved results than other methods.

中文翻译：

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一种新的基于粒子群智能的图像分割图分割技术

图像处理领域的进步需要改进图像分割方法。在图像分割中忽略光和噪声的影响，同时跟踪除此纹理之外的感兴趣对象也是自动分析图像的最重要因素之一。在各种分割方法中，基于图的技术因其生成准确分割结构的能力而被广泛使用。在本文中，我们提出了一种通过使用离散粒子群优化和多级分区来分割图像的新技术。与其他方法相比，所开发的技术具有更低的复杂性、更高的效率和更好的结果。