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SEMANTIC VIDEO RETRIEVAL USING HIGH LEVEL CONTEXT

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Date Issued:
2008
Abstract/Description:
Video retrieval – searching and retrieving videos relevant to a user defined query – is one of the most popular topics in both real life applications and multimedia research. This thesis employs concepts from Natural Language Understanding in solving the video retrieval problem. Our main contribution is the utilization of the semantic word similarity measures for video retrieval through the trained concept detectors, and the visual co-occurrence relations between such concepts. We propose two methods for content-based retrieval of videos: (1) A method for retrieving a new concept (a concept which is not known to the system and no annotation is available) using semantic word similarity and visual co-occurrence, which is an unsupervised method. (2) A method for retrieval of videos based on their relevance to a user defined text query using the semantic word similarity and visual content of videos. For evaluation purposes, we mainly used the automatic search and the high level feature extraction test set of TRECVID'06 and TRECVID'07 benchmarks. These two data sets consist of 250 hours of multilingual news video captured from American, Arabic, German and Chinese TV channels. Although our method for retrieving a new concept is an unsupervised method, it outperforms the trained concept detectors (which are supervised) on 7 out of 20 test concepts, and overall it performs very close to the trained detectors. On the other hand, our visual content based semantic retrieval method performs more than 100% better than the text-based retrieval method. This shows that using visual content alone we can have significantly good retrieval results.
Title: SEMANTIC VIDEO RETRIEVAL USING HIGH LEVEL CONTEXT.
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Name(s): Aytar, Yusuf, Author
Shah, Mubarak, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2008
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Video retrieval – searching and retrieving videos relevant to a user defined query – is one of the most popular topics in both real life applications and multimedia research. This thesis employs concepts from Natural Language Understanding in solving the video retrieval problem. Our main contribution is the utilization of the semantic word similarity measures for video retrieval through the trained concept detectors, and the visual co-occurrence relations between such concepts. We propose two methods for content-based retrieval of videos: (1) A method for retrieving a new concept (a concept which is not known to the system and no annotation is available) using semantic word similarity and visual co-occurrence, which is an unsupervised method. (2) A method for retrieval of videos based on their relevance to a user defined text query using the semantic word similarity and visual content of videos. For evaluation purposes, we mainly used the automatic search and the high level feature extraction test set of TRECVID'06 and TRECVID'07 benchmarks. These two data sets consist of 250 hours of multilingual news video captured from American, Arabic, German and Chinese TV channels. Although our method for retrieving a new concept is an unsupervised method, it outperforms the trained concept detectors (which are supervised) on 7 out of 20 test concepts, and overall it performs very close to the trained detectors. On the other hand, our visual content based semantic retrieval method performs more than 100% better than the text-based retrieval method. This shows that using visual content alone we can have significantly good retrieval results.
Identifier: CFE0002158 (IID), ucf:47521 (fedora)
Note(s): 2008-05-01
M.S.
Engineering and Computer Science, School of Electrical Engineering and Computer Science
Masters
This record was generated from author submitted information.
Subject(s): Semantic Video Retrieval
Video retrieval
High-level Context
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0002158
Restrictions on Access: campus 2009-04-01
Host Institution: UCF

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