A Multiple-Instance Learning Based Approach to Multimodal Data Mining

This paper presents multiple-instance learning based approach to multimodal data mining in a multimedia database. This approach is a highly scalable and adaptable framework that the authors call co-learning. Theoretic analysis and empirical evaluations demonstrate the advantage of the strong scalability and adaptability. Although this framework is general for multimodal data mining in any specific domain, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance in comparison with a state-of-the-art multimodal data mining method to showcase the promise of the co-learning framework. DOI: 10.4018/978-1-4666-0900-6.ch007