An automatic clustering algorithm for probability density functions
洪文良教授國立新竹教育大學應用數學系 下午 2:30–4:00 Tòa nhà Hồng Kinh 107 室摘要：
In this talk, 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 color image categorization.