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.
This work is supported in part by the RGC Grant #CUHK4176/97E. Portions of this manuscript have been presented in [8].
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King, I., Lau, TK. (1999). Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval. In: Perner, P., Petrou, M. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 1999. Lecture Notes in Computer Science(), vol 1715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48097-8_10
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DOI: https://doi.org/10.1007/3-540-48097-8_10
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