Minimal difference sampling for active learning image classification

被引：0

作者：

Wu, Jian ^{[1
]}

Sheng, Sheng-Li ^{[2
]}

Zhao, Peng-Peng ^{[1
]}

Cui, Zhi-Ming ^{[1
]}

机构：

[1] Institute of Intelligent Information Processing and Application, Soochow University

[2] Department of Computer Science, University of Central Arkansas

来源：

Tongxin Xuebao/Journal on Communications | 2014年 / 35卷 / 01期

关键词：

Active learning; Committee voting; Image classification; Minimal difference; Sampling strategy;

D O I：

10.3969/j.issn.1000-436x.2014.01.013

中图分类号：

学科分类号：

摘要：

Aiming at the problem of measuring the voting disagreement of committee, a minimal difference sampling method for image classification was proposed. It selects the sample with the minimal difference of two highest class probabilities voted by committee. The experimental results show that this method effectively enhances the classification accuracy compared with EQB and nEQB. Furthermore, the influence of the number of models in the decision-making committee was analyzed and discussed. The experimental results show that the proposed method always outperforms nEQB with the same number of models.

引用

页码：107 / 114

页数：7

共 22 条

[1]

Zhong H., Yang X.M., Jiao L.C., Texture classification based on multiresolution co-occurrence matrix, Journal of Computer Research and Development, 48, 11, pp. 1991-1999, (2011)

[2]

Ciresan D., Meier U., Schmidhuber J., Multi-column deep neural networks for image classification, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642-3649, (2012)

[3]

Xu Y., Zhu J.Y., Chang E., Et al., Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), pp. 964-971, (2012)

[4]

Volpi M., Tuia D., Kanevski M., Memory-based cluster sampling for remote sensing image classification, IEEE Transactions on Geoscience and Remote Sensing, 50, 8, pp. 3096-3106, (2012)

[5]

Settles B., Active Learning Literature Survey, (2010)

[6]

Lewis D.D., Catlett J., Heterogenous uncertainty sampling for supervised learning, Proceedings of International Conference on Machine Learning (ICML 1994), pp. 148-156, (1994)

[7]

Olsson F., A Literature Survey of Active Machine Learning in the Context of Natural Language Processing, (2009)

[8]

Fu Y., Zhu X., Li B., A survey on instance selection for active learning, Knowledge and Information Systems, 35, 2, pp. 249-283, (2013)

[9]

Joshi A.J., Porikli F., Papanikolopoulos N.P., Scalable active learning for multi-class image classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 11, pp. 2259-2273, (2012)

[10]

Li X., Guo Y., Adaptive active learning for image classification, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013), pp. 859-866, (2013)

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