標題: | Video segmentation using a histogram-based fuzzy c-means clustering algorithm |
作者: | Lo, CC Wang, SJ 資訊管理與財務金融系 註:原資管所+財金所 Department of Information Management and Finance |
關鍵字: | video segmentation;shot change detection;clustering;fuzzy c-means;histogram |
公開日期: | 1-一月-2001 |
摘要: | The purpose of video segmentation is to segment video sequence into shots where each shot represents a sequence of frames having the same contents, and then select key frames from each shot for indexing. Existing video segmentation methods can be classified into two groups: the shot change detection (SCD) approach for which thresholds have to be pre-assigned, and the clustering approach for which a prior knowledge of the number of clusters is required. In this paper, we propose a video segmentation method using a histogram-based fuzzy c-means (HBFCM) clustering algorithm. This algorithm is a hybrid of the two approaches aforementioned, and is designed to overcome the drawbacks of both approaches. The HBFCM clustering algorithm is composed of three phases: the feature extraction phase, the clustering phase, and the key-frame selection phase. In the first phase, differences between color histogram are extracted as features. In the second phase, the fuzzy c-means (FCM) is used to group features into three clusters: the shot change (SC) cluster, the suspected shot change (SSC) cluster, and the no shot change (NSC) cluster. In the last phase, shot change frames are identified from the SC and the SSC, and then used to segment video sequences into shots. Finally, key frames are selected from each shot Simulation results indicate that the HBFCM clustering algorithm is robust and applicable to various types of video sequences. |
URI: | http://hdl.handle.net/11536/150547 |
期刊: | 10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3: MEETING THE GRAND CHALLENGE: MACHINES THAT SERVE PEOPLE |
起始頁: | 920 |
結束頁: | 923 |
顯示於類別: | 會議論文 |