SBIR/STTR Research Award

SBIR Phase II: Multimodal Semantic Video Retrieval and Summarization

Award identifier 9dd8d73d6b84d9c3407fd8dce3b7c719b4bfcbfef699d85a94860a771e24bc56Awarding agency National Science Foundation

Award amount

$500,000

Program and phase

SBIR · Phase II

Award year

2011

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
Video Semantics LLC
Proposal award date
2011-04-01
Contract end date
2013-03-31
UEI
Not reported
Solicitation number
Not reported
Topic code
Phase II

Research abstract

This Small Business Innovation Research Phase II project will develop contextual video segmentation and automatic tagging technology and software. In long video streams that contain one or more topics, the software automatically discovers the beginnings and ends of Contextually-Coherent Video Segments in each video. Moreover, Video Semantics' technology automatically assigns textual tags to each segment such that these tags describe the topic discussed in that segment. The tags assigned make all parts of the video easily searchable. Large video producers currently depend on manually segmenting their content into small segments and assigning textual tags to these segments in order to make them searchable. A short advertisement is then inserted before each segment. This manual segmentation and tagging process represents a significant pain point for content producers because it is labor intensive and not cost effective. Meanwhile, government agencies, which continuously monitor video content depend on speech recognition to spot specified keywords. This approach inflicts two pain points: (i) analysts have to deal with large number of false detections because the context in which the keyword occurs might be irrelevant, and (ii) if the keyword occurs in an important context, analysts still need to scroll back and forth into the video to find the beginning of the relevant segment. Video Semantics' technology and products have the potential to efficiently address significant market needs. In addition to the commercial applications, the proposed technology will enable media monitoring agencies to perform their tasks more efficiently saving valuable analyst time and resources. Moreover, because Video Semantics? technology is language-independent, media monitoring agencies will be able to monitor more content in foreign languages without the need to develop language-specific technologies. The company will employ an indirect sales strategy via partnerships with software companies that develop media monitoring solutions and metadata generation tools. The company has identified its first customer and is working with them to integrate the contextual segmentation and tagging technology with their current media monitoring solutions.

Technology keywords

Not reported

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Agency intelligence

National Science Foundation

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