Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization

As a way to relieve the tedious work of manual annotation, active learning plays important roles in many applications of visual concept recognition. In typical active learning scenarios, the number of labelled data in the seed set is usually small. However, most existing active learning algorithms o...

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Bibliographic Details
Published in:International journal of computer vision 2015-06, Vol.113 (2), p.113-127
Main Authors: Yang, Yi, Ma, Zhigang, Nie, Feiping, Chang, Xiaojun, Hauptmann, Alexander G.
Format: Article
Language:English
Subjects:
Active learning
Algorithms
Analysis
Artificial Intelligence
Computer Imaging
Computer Science
Data mining
Exploitation
Image Processing and Computer Vision
Labeling
Optimization algorithms
Pattern Recognition
Pattern Recognition and Graphics
Studies
Vision
Vision systems
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Summary:As a way to relieve the tedious work of manual annotation, active learning plays important roles in many applications of visual concept recognition. In typical active learning scenarios, the number of labelled data in the seed set is usually small. However, most existing active learning algorithms only exploit the labelled data, which often suffers from over-fitting due to the small number of labelled examples. Besides, while much progress has been made in binary class active learning, little research attention has been focused on multi-class active learning. In this paper, we propose a semi-supervised batch mode multi-class active learning algorithm for visual concept recognition. Our algorithm exploits the whole active pool to evaluate the uncertainty of the data. Considering that uncertain data are always similar to each other, we propose to make the selected data as diverse as possible, for which we explicitly impose a diversity constraint on the objective function. As a multi-class active learning algorithm, our algorithm is able to exploit uncertainty across multiple classes. An efficient algorithm is used to optimize the objective function. Extensive experiments on action recognition, object classification, scene recognition, and event detection demonstrate its advantages.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-014-0781-x