Abstract
Multiple-instance Learning (MIL) is a new paradigm of supervised learning that deals with the classification of bags. Each bag is presented as a collection of instances from which features are extracted. In MIL, we have usually confronted with a large instance space for even moderately sized data sets since each bag may contain many instances. Hence it is important to design efficient instance pruning and selection techniques to speed up the learning process without compromising on the performance. In this paper, we address the issue of instance selection in multiple instance learning and propose the IS-MIL, an Instance Selection framework for MIL, to tackle large-scale MIL problems. IS-MIL is based on an alternative optimisation framework by iteratively repeating the steps of instance selection/ updating and classifier learning, which is guaranteed to converge. Experimental results demonstrate the utility and efficiency of the proposed approach compared to the alternatives.
Original language | English |
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Title of host publication | Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, Florida, USA, 20 – 25 June 2009 |
Publisher | IEEE |
Pages | 911-918 |
Number of pages | 8 |
ISBN (Print) | 9781424439928 |
DOIs | |
Publication status | Published - 2009 |
Event | IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Duration: 20 Jun 2009 → … |
Conference
Conference | IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Period | 20/06/09 → … |