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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
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
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Computer Science publications

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