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

Authors:
Irwin King;Tak-Kan Lau
Affiliations:
-;-
Venue:
MLDM '99 Proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition
Year:
1999

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Abstract

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.