A self-immunizing manifold ranking for image retrieval
Date
2013
Authors
Wu, J.
Li, Y.
Feng, S.
Shen, H.
Editors
Pei, J.
Tseng, V.
Cao, L.
Motoda, H.
Xu, G.
Tseng, V.
Cao, L.
Motoda, H.
Xu, G.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Lecture Notes in Artificial Intelligence, 2013 / Pei, J., Tseng, V., Cao, L., Motoda, H., Xu, G. (ed./s), vol.7819 LNAI, iss.PART 2, pp.426-436
Statement of Responsibility
Jun Wu, Yidong Li, Songhe Feng, and Hong Shen
Conference Name
17th Pacific-Asia Conference, PAKDD 2013 (14 Apr 2013 - 17 Apr 2013 : Gold Coast, Australia)
Abstract
Manifold ranking (MR), as a powerful semi-supervised learning algorithm, plays an important role to deal with the relevance feedback problem in content-based image retrieval (CBIR). However, conventional MR has two main drawbacks: 1) in many cases, it is prone to exploit “unreliable” unlabeled images when deployed in CBIR due to the semantic gap; 2) the performance of MR is quite sensitive to the scale parameter used for calculating the Laplacian matrix. In this work, a self-immunizing MR approach is presented to address the drawbacks. Concretely, we first propose an elastic kNN graph as well as its constructing algorithm to exploit unlabeled images “safely”, and then develop a local scaling solution to calculate the Laplacian matrix adaptively. Extensive experiments on 10,000 Corel images show that the proposed algorithm is more effective than the state-of-the-art approaches.
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© Springer-Verlag Berlin Heidelberg 2013