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|Title:||A self-immunizing manifold ranking for image retrieval|
|Citation:||Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II, 2013 / Pei, J., Tseng, V., Cao, L., Motoda, H., Xu, G. (ed./s), vol.7819 LNAI, iss.PART 2, pp.426-436|
|Series/Report no.:||Lecture Notes in Computer Science; 7819|
|Conference Name:||17th Pacific-Asia Conference, PAKDD 2013 (14 Apr 2013 - 17 Apr 2013 : Gold Coast, Australia)|
|Jun Wu, Yidong Li, Songhe Feng, and Hong Shen|
|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.|
|Keywords:||content-based image retrieval; relevance feedback; self-immunizing manifold ranking; elastic kNN graph; local scaling|
|Rights:||© Springer-Verlag Berlin Heidelberg 2013|
|Appears in Collections:||Computer Science publications|
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