Unsupervised estimation of segmentation quality using nonnegative factorization

We propose an unsupervised method for evaluating image segmentation. Common methods are typically based on evaluating smoothness within segments and contrast between them, and the measure they provide is not explicitly related to segmentation errors. The proposed approach differs from these methods on several important points and has several advantages over them. First, it provides a meaningful, quantitative assessment of segmentation quality, in precision/recall terms, which were applicable so far only for supervised evaluation. Second, it builds on a new image model, which characterizes the segments as a mixture of basic feature distributions. The precision/recall estimates are then obtained by a nonnegative matrix factorization (NMF) process. A third important advantage is that the estimates, which are based on intrinsic properties of the specific image being evaluated and not on a comparison to typical images (learning), are relatively robust to context factors such as image quali...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Roman Sandler, Michael Lindenbaum
• Keywords: Computer Vision, CVPR 2008, Image Segmentation, Nonnegative Matrix Factorization, Segmentation Algorithm, Segmentation Errors, Segmentation Quality