Interval fuzzy spectral clustering ensemble algorithm for color image segmentation

In recent years, spectral clustering algorithm has been widely used in the field of pattern recognition and computer vision. How to construct an effective similarity matrix is the key issue of spectral clustering algorithm. In order to describe the uncertainty in the image and design the efficient similarity matrix for spectral clustering, an interval fuzzy spectral clustering ensemble algorithm for color image segmentation (IFSCE) is presented in this paper. Firstly, the color histogram is obtained by the just noticeable difference color threshold method. Then the interval fuzzy similarity measure based on color feature is constructed by utilizing the interval membership degree and the image are grouped by normalized cut criterion under the similarity matrix produced by interval fuzzy similarity measure. Finally, the segmentation results with different optimal fuzzy factors combination are integrated to get the final result. The experimental results on real images show that the proposed algorithm behaves well in the segmentation accuracy and visual segmentation result.