Full metadata record
DC FieldValueLanguage
dc.contributor.authorLo, CCen_US
dc.contributor.authorWang, SJen_US
dc.date.accessioned2014-12-08T15:43:17Z-
dc.date.available2014-12-08T15:43:17Z-
dc.date.issued2001-11-01en_US
dc.identifier.issn0920-5489en_US
dc.identifier.urihttp://dx.doi.org/10.1016/S0920-5489(01)00085-Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/29294-
dc.description.abstractThe 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. (C) 2001 Elsevier Science B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectkey frameen_US
dc.subjectvideo segmentationen_US
dc.subjectshot change detectionen_US
dc.subjectclusteringen_US
dc.subjectfuzzy c-meansen_US
dc.subjecthistogramen_US
dc.titleVideo segmentation using a histogram-based fuzzy c-means clustering algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/S0920-5489(01)00085-Xen_US
dc.identifier.journalCOMPUTER STANDARDS & INTERFACESen_US
dc.citation.volume23en_US
dc.citation.issue5en_US
dc.citation.spage429en_US
dc.citation.epage438en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000171992600005-
dc.citation.woscount15-
Appears in Collections:Articles


Files in This Item:

  1. 000171992600005.pdf

If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.