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MLDM
1999
Springer

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

10 years 1 months ago
Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval
In large content-based image database applications, e cient information retrieval depends heavily on good indexing structures of the extracted features. While indexing techniques for text retrieval are well understood, e cient 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 e cient. 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.
Irwin King, Tak-Kan Lau
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1999
Where MLDM
Authors Irwin King, Tak-Kan Lau
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