Segmentation via Incremental Transductive Learning

摘要：

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.

关键词： segmentation clustering transductive learning

作者: Rui Huang Nong Sang Qiling Tang

作者单位: Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan, 430074, P.R. China

会议类型: 国际会议

会议名称: The Fifth International Conference on Image and Graphics(第五届国际图像图形学学术会议 ICIG 2009)

会议地点: 西安

会议语种:英文

页码: 213-216

在线出版日期: 2009-09-20（万方平台首次上网日期，不代表论文的发表时间）