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Semantic web for content based video...
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Chittillappily Sebastine, Sancho.
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Semantic web for content based video retrieval.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Semantic web for content based video retrieval./
作者:
Chittillappily Sebastine, Sancho.
面頁冊數:
50 p.
附註:
Source: Masters Abstracts International, Volume: 48-06, page: 3872.
Contained By:
Masters Abstracts International48-06.
標題:
Information Technology. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1485098
ISBN:
9781124085135
Semantic web for content based video retrieval.
Chittillappily Sebastine, Sancho.
Semantic web for content based video retrieval.
- 50 p.
Source: Masters Abstracts International, Volume: 48-06, page: 3872.
Thesis (M.S.)--The University of Texas at Dallas, 2010.
This work aims to provide a semantic web based framework for content-based video retrieval. Currently, we do not have scalable integration platforms to represent extracted features from videos, so that they could be indexed and searched. The task of indexing extracted features from videos is a difficult challenge, due to the diverse nature of the features and the temporal dimensions of videos. We present a semantic web based framework for automatic feature extraction, storage, indexing and retrieval of videos. Videos are represented as interconnected set of semantic resources. Furthermore, we suggest a new ranking algorithm for finding related resources which could be used in a semantic web based search engine. Finally, we define an algorithm for refinement of noise keywords to improve the quality of keywords.
ISBN: 9781124085135Subjects--Topical Terms:
1030799
Information Technology.
Semantic web for content based video retrieval.
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This work aims to provide a semantic web based framework for content-based video retrieval. Currently, we do not have scalable integration platforms to represent extracted features from videos, so that they could be indexed and searched. The task of indexing extracted features from videos is a difficult challenge, due to the diverse nature of the features and the temporal dimensions of videos. We present a semantic web based framework for automatic feature extraction, storage, indexing and retrieval of videos. Videos are represented as interconnected set of semantic resources. Furthermore, we suggest a new ranking algorithm for finding related resources which could be used in a semantic web based search engine. Finally, we define an algorithm for refinement of noise keywords to improve the quality of keywords.
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