MISSL: multiple-instance semi-supervised learning

There has been much work on applying multiple-instance (MI) learning to contentbased image retrieval (CBIR) where the goal is to rank all images in a known repository using a small labeled data set. Most existing MI learning algorithms are nontransductive in that the images in the repository serve only as test data and are not used in the learning process. We present MISSL (Multiple-Instance Semi-Supervised Learning) that transforms any MI problem into an input for a graph-based single-instance semisupervised learning method that encodes the MI aspects of the problem simultaneously working at both the bag and point levels. Unlike most prior MI learning algorithms, MISSL makes use of the unlabeled data.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Rouhollah Rahmani, Sally A. Goldman
• Keywords: ICML 2006, Machine Learning, MI Learning Algorithms, Most Existing Mi, Multiple-Instance Semi-Supervised Learning