Laplacian regularized active learning for image segmentation
Lianbo Zhang, Dapeng Tao, Weifeng Liu · 2014
Image segmentation is a common topic in image processing. Many methods has been used in image segmentation, such as Graph cut, threshold-based. However, these methods can't work with high precision. Among these method, SVM is used as a good tool for classification, as we treat image segmentation as a problem of classification. To solve the problem above and get better segmentation result as well as high precision, we add Laplacian regularization to SVM algorithm to get a new algorithm i.e. Laplacian regularized active learning for image segmentation. Our algorithm considers distance between pixels when segmenting a picture, which is executed by Laplacian regularization. Experiments demonstrate that our algorithm perform better in comparison with common SVM algorithm.