Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/87255
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Type: | Conference paper |
Title: | Incorporating manifold ranking with active learning in relevance feedback for image retrieval |
Author: | Wu, J. Li, Y. Sang, Y. Shen, H. |
Citation: | Proceedings: 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies: PDCAT 2012, 2012 / Shen, H., Sang, Y., Li, Y., Qian, D., Zomaya, A. (ed./s), pp.739-744 |
Publisher: | IEEE |
Issue Date: | 2012 |
ISBN: | 9780769548791 |
Conference Name: | 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2012) (14 Dec 2012 - 16 Dec 2012 : Beijing, China) |
Editor: | Shen, H. Sang, Y. Li, Y. Qian, D. Zomaya, A. |
Statement of Responsibility: | Jun Wu, Yidong Li, Yingpeng Sang, Hong Shen |
Abstract: | Combining manifold ranking with active learning (MRAL for short) is one popular and successful technique for relevance feedback in content-based image retrieval (CBIR). Despite the success, conventional MRAL has two main drawbacks. First, the performance of manifold ranking is very sensitive to the scale parameter used for calculating the Laplacian matrix. Second, conventional MRAL does not take into account the redundancy among examples and thus could select multiple examples that are similar to each other. In this work, a novel MRAL framework is presented to address the drawbacks. Concretely, we first propose a self-tuning manifold ranking algorithm that can adaptively calculate the Laplacian matrix via a local scaling mechanism, and then develop a hybrid active learning algorithm by integrating three well-known selective sampling criteria, which is able to effectively and efficiently identify the most informative and diversified examples for the user to label. Experiments on 10,000 Corel images show that the proposed method is significantly more effective than some existing approaches. |
Keywords: | Image retrieval; relevance feedback; manifold ranking; active learning |
Rights: | ©2012 IEEE |
DOI: | 10.1109/PDCAT.2012.82 |
Published version: | http://dx.doi.org/10.1109/pdcat.2012.82 |
Appears in Collections: | Aurora harvest 2 Computer Science publications |
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RA_hdl_87255.pdf Restricted Access | Restricted Access | 407.59 kB | Adobe PDF | View/Open |
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