Content Based Image Retrieval Based on a Nonlinear Similarity Model

  • Guang-Ho Cha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3980)


In this paper, we propose a new nonlinear paradigm to clustering, indexing and searching for content-based image retrieval (CBIR). The scheme is designed for approximate searches and all the work is performed in a transformed feature space. We first (1) map the input space into a feature space via a nonlinear map, (2) compute the top eigenvectors in that feature space, and (3) capture cluster structure based on the eigenvectors. We (4) describe each cluster with a minimal hypersphere containing all objects in the cluster, (5) derive the similarity measure for each cluster individually and (6) construct a bitmap index for each cluster. Finally we (7) model the similarity query as a hyper-rectangular range query and search the clusters near the query point. Our preliminary experimental results for our new framework demonstrate considerable effectiveness and efficiency in CBIR.


Feature Space Spectral Cluster Indexing Method Query Point Content Base Image Retrieval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guang-Ho Cha
    • 1
  1. 1.Department of Computer EngineeringSeoul National University of TechnologySeoulSouth Korea

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