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CLC number: TP391.4

On-line Access: 2010-11-04

Received: 2010-09-14

Revision Accepted: 2010-09-30

Crosschecked: 2010-09-14

Cited: 3

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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.11 P.860-871


Multi-task multi-label multiple instance learning

Author(s):  Yi Shen, Jian-ping Fan

Affiliation(s):  Department of Computer Science, University of North Carolina at Charlotte 28223, USA

Corresponding email(s):   yshen9@uncc.edu, jfan@uncc.edu

Key Words:  Object network, Loosely tagged images, Multi-task learning, Multi-label learning, Multiple instance learning

Yi Shen, Jian-ping Fan. Multi-task multi-label multiple instance learning[J]. Journal of Zhejiang University Science C, 2010, 11(11): 860-871.

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author="Yi Shen, Jian-ping Fan",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

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%T Multi-task multi-label multiple instance learning
%A Yi Shen
%A Jian-ping Fan
%J Journal of Zhejiang University SCIENCE C
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%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1001005

T1 - Multi-task multi-label multiple instance learning
A1 - Yi Shen
A1 - Jian-ping Fan
J0 - Journal of Zhejiang University Science C
VL - 11
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SP - 860
EP - 871
%@ 1869-1951
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1001005

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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