The L-K means hierarchy clustering is proposed to decrease the long time of searching in a huge scale face database by reducing retrieving range. The database is divided into several sub sets after clustering. One-step split is used to divide the massive sub set into a few of small sets. Data redundant technology and predefined thresh are worked on a class's edge elements which will be re-signed into some categories for correctly searching in sets. The experiment results show that the proposed methods can effectively improve the searching speed while ensuring similar retrieval accuracy to search in the whole database.
['Xiaohua Liu', 'Chunguang Zhou', 'Libiao Zhang', 'Huipeng Sheng', 'Jiangchun Li']
Quick Searching Based on L-K Means Hierarchical Clustering in a Huge Scale Face Database