High-speed MRF-based segmentation algorithm using pixonal images

Abstract

Segmentation is one of the most complicated procedures in the image processing that has important role in the image analysis. In this paper, an improved pixon-based method for image segmentation is proposed. In proposed algorithm, complex partial differential equations (PDEs) is used as a kernel function to make pixonal image. Using this kernel function causes noise on images to reduce and an image not to be over-segment when the pixon-based method is used. Utilising the PDE-based method leads to elimination of some unnecessary details and results in a fewer pixon number, faster performance and more robustness against unwanted environmental noises. As the next step, the appropriate pixons are extracted and eventually, we segment the image with the use of a Markov random field. The experimental results indicate that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-image segmentation techniques. To evaluate the proposed algorithm and compare it with the last best algorithms, many experiments on standard images were performed. The results indicate that the proposed algorithm is faster than other methods, with the most segmentation accuracy.

Keywords:

• Markov random field
• image segmentation
• pixonal image
• Gibbs distribution