A framework for temporal similarity measures of content-based scene retrieval
| dc.citation.epage | 532 | en_US |
| dc.citation.issue | 5 | en_US |
| dc.citation.spage | 517 | en_US |
| dc.citation.volume | 22 | en_US |
| dc.citation.woscount | 1 | |
| dc.contributor.author | Shan, MK | en_US |
| dc.contributor.author | Lee, SY | en_US |
| dc.contributor.department | 資訊科學與工程研究所 | zh_TW |
| dc.contributor.department | Institute of Computer Science and Engineering | en_US |
| dc.date.accessioned | 2014-12-08T15:43:58Z | |
| dc.date.available | 2014-12-08T15:43:58Z | |
| dc.date.issued | 2001-04-01 | en_US |
| dc.description.abstract | The distinguished features of video retrieval lie in the similarity measures and content-based retrieval. Most research on content-based video retrieval represents the content of video as a set of frames, leaving out the temporal ordering of frames in the shot. In this paper, the similarity measures of video content are investigated. We propose a series of similarity measures based on the similarity of frame sequence which take temporal ordering into consideration. All the algorithms corresponding to the similarity measures are based on the approach of dynamic programming. (C) 2001 Published by Elsevier Science B.V. | en_US |
| dc.identifier.doi | 10.1016/S0167-8655(00)00120-3 | en_US |
| dc.identifier.issn | 0167-8655 | en_US |
| dc.identifier.journal | PATTERN RECOGNITION LETTERS | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1016/S0167-8655(00)00120-3 | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/29734 | |
| dc.identifier.wosnumber | WOS:000168148700008 | |
| dc.language.iso | en_US | en_US |
| dc.subject | content-based video retrieval | en_US |
| dc.subject | similarity measure | en_US |
| dc.subject | sequence mapping | en_US |
| dc.subject | dynamic programming | en_US |
| dc.title | A framework for temporal similarity measures of content-based scene retrieval | en_US |
| dc.type | Article | en_US |