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Frontiers of Information Technology & Electronic Engineering  2010, Vol. 11 Issue (11): 860-871    DOI: 10.1631/jzus.C1001005
    
Yi Shen, Jian-ping Fan
Multi-task multi-label multiple instance learning
Yi Shen, Jian-ping Fan
Department of Computer Science, University of North Carolina at Charlotte 28223, USA
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Abstract: For automatic object detection tasks, large amounts of training images are usually labeled to achieve more reliable training of the object classifiers; this is cost-expensive since it requires hiring professionals to label large-scale training images. When a large number of object classes come into view, the issue of obtaining a large enough amount of the labeled training images becomes more critical. There are three potential solutions to reduce the burden for image labeling: (1) allowing people to provide the object labels loosely at the image level rather than at the object level (e.g., loosely-tagged images without identifying the exact object locations in the images); (2) harnessing large-scale collaboratively-tagged images that are available on the Internet; and, (3) developing new machine learning algorithms that can directly leverage large-scale collaboratively- or loosely-tagged images for achieving more effective training of a large number of object classifiers. Based on these observations, a multi-task multi-label multiple instance learning (MTML-MIL) algorithm is developed in this paper by leveraging both inter-object correlations and large-scale loosely-labeled images for object classifier training. By seamlessly integrating multi-task learning, multi-label learning, and multiple instance learning, our MTML-MIL algorithm can achieve more accurate training of a large number of inter-related object classifiers (where an object network is constructed for determining the inter-related learning tasks directly in the feature space rather than in the label space). Our experimental results have shown that our MTML-MIL algorithm can achieve higher detection accuracy rates for automatic object detection.
Key words: Object network    Loosely tagged images    Multi-task learning    Multi-label learning    Multiple instance learning
收稿日期: 2010-09-14 出版日期: 2010-11-04
CLC:  TP391.4  
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Yi Shen, Jian-ping Fan. Multi-task multi-label multiple instance learning. Front. Inform. Technol. Electron. Eng., 2010, 11(11): 860-871.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1001005    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2010/V11/I11/860

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