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 ...
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.
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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