A fast hierarchical approach to image segmentation

In this paper, we propose a hierarchical approach to image segmentation based on the use of a graph regularisation algorithm. The initial segmentation map is obtained using the normalized cut segmentation algorithm. We then refine the segmentation results by iteratively propagating the class-labels from coarse-to-fine sampling levels. Image segmentation at each intermediate level is recast as a constrained graph regularisation problem that can be solved efficiently. The multi-level nature of our method achieves low computational cost and robustness to noise corruption. We provide experimental results on the Berkeley Image Database and show the efficacy of our method for segmentation of high resolution images.