Correlated Label Propagation with Application to Multi-label Learning

Many computer vision applications, such as scene analysis and medical image interpretation, are ill-suited for traditional classification where each image can only be associated with a single class. This has stimulated recent work in multi-label learning where a given image can be tagged with multiple class labels. A serious problem with existing approaches is that they are unable to exploit correlations between class labels. This paper presents a novel framework for multi-label learning termed Correlated Label Propagation (CLP) that explicitly models interactions between labels in an efficient manner. As in standard label propagation, labels attached to training data points are propagated to test data points; however, unlike standard algorithms that treat each label independently, CLP simultaneously co-propagates multiple labels. Existing work eschews such an approach since naive algorithms for label co-propagation are intractable. We present an algorithm based on properties of submo...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Feng Kang, Rong Jin, Rahul Sukthankar
• Keywords: Computer Vision, Correlated Label Propagation, CVPR 2006, Medical Image Interpretation, Multiple Class Labels, Multiple Labels, Standard Label Propagation