Color image segmentation using fuzzy min-max neural networks

In this work, a new color image segmentation method, based on fuzzy min-max neural networks is presented. The proposed method is called FMMIS (fuzzy min-max neural network for image segmentation). The FMMIS method grows boxes from a set of seed pixels, to find the minimum bounded rectangle (MBR) for each object present in the images. The algorithm was tested on wood images of 10 defect categories and with images of frontal faces taken from the FERET database. The FMMIS algorithm outperformed alternative methods in terms of object detection rate, false positive detection rate, average execution time and the RUMA index. The proposed method is very fast and it may be applied to real-time image segmentation tasks.

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