A universal texture segmentation and representation scheme based on ant colony optimization for iris image processing


This paper proposes a novel scheme for texture segmentation and representation based on Ant Colony Optimization (ACO). Texture segmentation and texture characteristic expression are two important areas in image pattern recognition. Nevertheless, until now, how to find an effective way for accomplishing these tasks is still a major challenge in practical applications such as iris image processing. We propose a framework for ACO based image processing methods. Considering the specific characteristics of various tasks, such a framework possesses the flexibility of only defining different criteria for ant behavior correspondingly. By defining different kinds of direction probability and movement difficulty for artificial ants, an ACO based image segmentation algorithm and a texture representation method are then presented for automatic iris image processing. Experimental results demonstrated that the ACO based image processing methods are competitive and quite promising, with excellent effectiveness and practicability especially for images with complex local texture situations.

In our iris study, image segmentation is regarded as outlining the regions of pixels with a similar background texture. For each pixel in an image, its brightness and gradient, together with neighboring pixel’s brightness and gradient are taken as local texture features. For ACO, the movement of artificial ants is influenced by these local texture features, and the global pheromone distribution on the image of a large number of artificial ants reveals the region segmentation and texture representation results. After segmentation, texture representation for some specific regions are compared between normal people and patients suffering the corresponding disease. Experimental results proved not only the effectiveness of ACO in image segmentation, but also proved the discriminatibility of the ACO based texture representation.