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Content Provider | IEEE Xplore Digital Library |
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Author | Ran Chang Xiaojun Qi |
Copyright Year | 2011 |
Description | Author affiliation: Computer Science Department, Utah State University, Logan, UT 84322-4205 (Ran Chang; Xiaojun Qi) |
Abstract | We propose a novel weighted manifold-ranking based image retrieval method to improve the effectiveness of traditional manifold methods. Specifically, we apply the SVM-based relevance feedback technique to create semantic clusters for computing the reliability score of each database image. We then incorporate the reliability scores into the affinity matrix to construct a weighted manifold structure. We finally create an asymmetric relevance vector to store users' positively and negatively labeled information. Our system ensures to propagate the labels in the relevance vector to the images with high reliability scores and discriminately spread the ranking scores of positive and negative images via the weighted manifold structure. Extensive experiments demonstrate our system outperforms the other manifold systems and SVM-based systems in the context of both correct and erroneous feedback. |
Starting Page | 2425 |
Ending Page | 2428 |
File Size | 264728 |
Page Count | 4 |
File Format | |
ISBN | 9781457713040 |
ISSN | 15224880 |
e-ISBN | 9781457713033 |
e-ISBN | 9781457713026 |
DOI | 10.1109/ICIP.2011.6116133 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-09-11 |
Publisher Place | Belgium |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Manifolds Reliability Semantics Image retrieval Support vector machines Training weighted manifold Content based image retrieval (CBIR) backbone image semantic clusters |
Content Type | Text |
Resource Type | Article |
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