Segmentation via Incremental Transductive Learning
Rui Huang, Nong Sang, Qiling Tang · 2009
In this paper, we propose a novel unsupervised clustering method for feature space analysis. We combine mean shift with a transductive learning method, semi-supervised discriminant analysis (SDA), in an incremental learning scheme. We use mean shift clustering to generate the class label, and use SDA to do subspace selection. Both these steps are performed alternately. Our clustering result could maintain good spatial consistency for all data in feature space. On image segmentation, we directly apply our clustering method to the L*a*b* color feature space generated from superpixels, and set each pixel with the clustering label of its superpixel. We test our image segmentation method on Berkeley image data set.