Abstract
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.
Highlights
The partitioning and analysis of an image segmentation are the most imperative steps
For segmenting an image by swarm intelligence-based technique, graphical structure of an image is generated, in which pixels of an image are the vertices of the graph and weight of an edge in the graph is the subtraction of pixel intensities of the connecting vertices
Segmentation results of six test images from the Berkeley dataset are generated by using proposed multilevel recursive discrete particle swarm optimization algorithm (MRDPSO) algorithm and by other three methods; normalized cut (N-Cut) [16], technique based on minimal spanning tree (PMST) [17], and fuzzy rulebased approach (FR-Cut) [18]
Summary
The partitioning and analysis of an image segmentation are the most imperative steps. Most of the applications demand for very precise and computationally efficient image processing techniques. Graphical structure of an image is more flexible and computationally efficient way for the formulation of image segmentation problem, whereas swarm intelligence techniques enhance the process of graph partitioning. Wu et al [1] have used min-cut for the clustering method It works well only for small groups of remote nodes in the graph, but for the dense regions it generates poor quality of segmentation. To address this peculiar unfairness for partitioning, Shi et al developed a new metric of disassociation, the normalized cut N-Cut [2]. Algebraic multigrid approach [3] is the added advantage to increase the efficiency of normalized cut
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