SBIR Phase I: Multimodal Semantic Video Retrieval and Summarization

Award Information
Agency:
National Science Foundation
Branch
n/a
Amount:
$100,000.00
Award Year:
2009
Program:
SBIR
Phase:
Phase I
Contract:
0912519
Award Id:
91050
Agency Tracking Number:
0912519
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
6565 Cedar Ln, Columbia, MD, 21044
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
826551892
Principal Investigator:
WaelAbd-Almageed
PhD
(301) 318-6427
wamageed@videosemantics.com
Business Contact:
WaelAbd-Almageed
PhD
(301) 318-6427
wamageed@videosemantics.com
Research Institute:
n/a
Abstract
This Small Business Innovation Research (SBIR) Phase I project aims to develop methodology and software for highly accurate and efficient semantic video retrieval and summarization. Video Semantics will, provide personalized summaries of video content that meet users' preferences. These summaries will be based on shot granularity instead of the widely used key-frame-based summaries that are oblivious of semantics. Additionally, the proposed technology will significantly enhance online video search by enabling users to retrieve only semantically-relevant shots instead of the entire video. The key component of the software is an automated semantic video annotation system that integrates realtime video shot detection, speech recognition, natural language processing, and logic inference methods to accurately select video shots according to semantic user requests and preferences. Consumers and video content service providers will use the proposed adaptive video messaging technique to efficiently communicate queries, preferences and results using Semantic Video Summary messages (SVS). The proposed software, once commercialized, can affect a shift in the way online video content is searched and retrieved. Moreover, if successful, the software will advance the state-of-the-art of constructing video summaries, which in contrast to current technologies, accurately responds to semantic level user queries. Consequently, the software may be of interest to numerous content providers and consumers to be employed in a multitude of video applications. The software could also be integrated into the ever-popular digital video recorders to enable the owner to search large volumes of archived videos and retrieve specific ones given semantic queries, rather than the usually inaccurate file names. On the other hand, the unique summarization capabilities of the software can be used by content/service providers where personalized, semantic-based summary criteria can be predefined by the user so that the content providers, adaptively (based on network and device capabilities) stream summaries matching users' requirements to their smart phones of other mobile devices. This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

* information listed above is at the time of submission.

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