Region-Based Image Clustering and Retrieval Using Multiple Instance Learning

Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). This is an area where a huge number of image regions are involved. For the sake of efficiency, we adopt a Genetic Algorithm based clustering method to reduce the search space. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. Performance is evaluated and the effectiveness of our retrieval algorithm is demonstrated in comparative studies.

• Type: Conference
• Year: 2005
• Where: CIVR
• Authors: Chengcui Zhang, Xin Chen
• Keywords: CIVR 2005, Content Based Image Retrieval, Multiple Instance Learning, Supervised Learning Problem