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.

• Type: Conference
• Year: 1999
• Where: MLDM
• Authors: Irwin King, Tak-Kan Lau
• Keywords: Cient Information Retrieval, Indexing Structure, Machine Learning, MLDM 1999, Robust Indexing Methodology