Interactive Segmentation Using Constrained Laplacian Optimization
We present a novel interactive image segmentation approach with user scribbles using constrained Laplacian graph optimization. A novel energy framework is developed by adding the smoothing item in the cost function of Laplacian graph energy. To the best of our knowledge, our approach is the first to incorporate the normalized cuts and graph cuts algorithms into a unified energy optimization framework. The proposed approach is further accelerated by running the proposed optimization method on a band region when we segment the large images. Our acceleration strategy enables our approach to efficiently segment the large images, which yields about a 20-80 times speedup. The proposed approach is evaluated on both the publicly available data sets and our own data set with large images. The benefits of the proposed unified framework are also demonstrated both qualitatively and quantitatively. The experimental results show that our segmentation method achieves better performance of both boundary recall and error rate than the existing state-of-the-art approaches.