Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval

  • Irwin King
  • Tak-Kan Lau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1715)


In large content-based image database applications, efficient information retrieval depends heavily on good indexing structures of the extracted features. While indexing techniques for text retrieval are well understood, efficient and robust indexing methodology for image retrieval is still in its infancy. In this paper, we present a non-hierarchical clustering scheme for index generation using the Rival Penalized Competitive Learning (RPCL) algorithm. RPCL is a stochastic heuristic clustering method which provides good cluster center approximation and is computationally efficient. Using synthetic data as well as real data, we demonstrate the recall and precision performance measurement of nearest-neighbor feature retrieval based on the indexing structure generated by RPCL.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Irwin King
    • 1
  • Tak-Kan Lau
    • 1
  1. 1.Department of Computer Science & EngineeringThe Chinese University of Hong KongNew TerritoriesHong Kong

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