Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

• A novel genetic algorithm based on spatial fuzzy C-mean (sFCM) method has been proposed to make compact and well separated clusters.
• Fuzzy separation and global compactness are considered the two objective functions to be optimized simultaneously.
• The self organizing map (SOM) is used to automatically determine the optimal number of clusters in order to set the length of chromosome.
• A progressive technique is used to handle the initialization problem. The algorithm considers some small parts of an image as separate segments which leads to over-segmentation.
• A novel merging technique is proposed to address the over-segmentation problem.

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.

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