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A Self-immunizing Manifold Ranking for Image Retrieval

  • Jun Wu
  • Yidong Li
  • Songhe Feng
  • Hong Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)

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 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jun Wu
    • 1
  • Yidong Li
    • 1
  • Songhe Feng
    • 1
  • Hong Shen
    • 1
    • 2
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  2. 2.School of Computer ScienceUniversity of AdelaideAustralia

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