Large-Scale Video Database Retrieval via Visual Recommendation: Bridge the interest and knowledge gaps via visual analytics

 
9783639105711: Large-Scale Video Database Retrieval via Visual Recommendation: Bridge the interest and knowledge gaps via visual analytics
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Motivated by Google's great success on text document retrieval, researchers began to build a new generation of video retrieval systems supporting semantic video retrieval via keywords. But these systems are unable to provide satisfactory results because of several challenging problems. First, existing semantic understanding techniques are still immature, and their performance is not good enough to enable keyword-based retrieval. Second, the mismatch between visual concepts and keywords prevents any keyword-based search engines from retrieving visual concepts that are difficult to represent in language. Third, users may not have a clear idea of their needs at the beginning for many multimedia queries. Therefore, they may not be able to represent their preference with keywords. To resolve these problems, we have proposed a novel systematic solution via visual recommendation. It integrates the latest achievements of semantic video analysis, knowledge discovery and visual analytics and optimized all components toward a single target. As a result, the proposed system is able to implement more efficient and intuitive video retrieval.

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Hangzai Luo is an associate professor at the East China Normal University. Jianping Fan is an associate professor at the University of North Carolina at Charlotte. They were the second place winner of the Director of Research's Award from the Department of Homeland Security of USA for achievements on large-scale video database retrieval.

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Book Description Condition: New. Publisher/Verlag: VDM Verlag Dr. Müller | Bridge the interest and knowledge gaps via visual analytics | Motivated by Google's great success on text document retrieval, researchers began to build a new generation of video retrieval systems supporting semantic video retrieval via keywords. But these systems are unable to provide satisfactory results because of several challenging problems. First, existing semantic understanding techniques are still immature, and their performance is not good enough to enable keyword-based retrieval. Second, the mismatch between visual concepts and keywords prevents any keyword-based search engines from retrieving visual concepts that are difficult to represent in language. Third, users may not have a clear idea of their needs at the beginning for many multimedia queries. Therefore, they may not be able to represent their preference with keywords. To resolve these problems, we have proposed a novel systematic solution via visual recommendation. It integrates the latest achievements of semantic video analysis, knowledge discovery and visual analytics and optimized all components toward a single target. As a result, the proposed system is able to implement more efficient and intuitive video retrieval. | Format: Paperback | Language/Sprache: english | 144 gr | 96 pp. Seller Inventory # K9783639105711

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Book Description VDM Verlag, Germany, 2008. Paperback. Condition: New. Language: English . Brand New Book ***** Print on Demand *****.Motivated by Google s great success on text document retrieval, researchers began to build a new generation of video retrieval systems supporting semantic video retrieval via keywords. But these systems are unable to provide satisfactory results because of several challenging problems. First, existing semantic understanding techniques are still immature, and their performance is not good enough to enable keyword-based retrieval. Second, the mismatch between visual concepts and keywords prevents any keyword-based search engines from retrieving visual concepts that are difficult to represent in language. Third, users may not have a clear idea of their needs at the beginning for many multimedia queries. Therefore, they may not be able to represent their preference with keywords. To resolve these problems, we have proposed a novel systematic solution via visual recommendation. It integrates the latest achievements of semantic video analysis, knowledge discovery and visual analytics and optimized all components toward a single target. As a result, the proposed system is able to implement more efficient and intuitive video retrieval. Seller Inventory # AAV9783639105711

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Book Description VDM Verlag. Paperback. Condition: New. 96 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.Motivated by Googles great success on text document retrieval, researchers began to build a new generation of video retrieval systems supporting semantic video retrieval via keywords. But these systems are unable to provide satisfactory results because of several challenging problems. First, existing semantic understanding techniques are still immature, and their performance is not good enough to enable keyword-based retrieval. Second, the mismatch between visual concepts and keywords prevents any keyword-based search engines from retrieving visual concepts that are difficult to represent in language. Third, users may not have a clear idea of their needs at the beginning for many multimedia queries. Therefore, they may not be able to represent their preference with keywords. To resolve these problems, we have proposed a novel systematic solution via visual recommendation. It integrates the latest achievements of semantic video analysis, knowledge discovery and visual analytics and optimized all components toward a single target. As a result, the proposed system is able to implement more efficient and intuitive video retrieval. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783639105711

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