Taylor and Francis Ltd.;Abingdon: Taylor & Francis
摘要:
摘要： We propose an intuitive and computationally simple algorithm for clustering the probability density functions (pdfs). A data-driven learning mechanism is incorporated in the algorithm in order to determine the suitable widths of the clusters. The clustering results prove that the proposed algorithm is able to automatically group the pdfs and provide the optimal cluster number without any a priori information. The performance study also shows that the proposed algorithm is more efficient than existing ones. In addition, the clustering can serve as the intermediate compression tool in content-based multimedia retrieval that we apply the proposed algorithm to categorize a subset of COREL image database. And the clustering results indicate that the proposed algorithm performs well in colour image categorization. 出版者： Abingdon: Taylor & Francis 出版日期： 2015-10-13 出處： Journal of statistical computation and simulation, 2015-10, Vol.85 (15), p.3047-3063 版權： 2014 Taylor & Francis 2014 版權： Copyright Taylor & Francis Ltd. 2015 識別號： ISSN: 0094-9655 識別號： EISSN: 1563-5163 識別號： DOI: 10.1080/00949655.2014.949715
