Leveraging an image folksonomy and the Signature Quadratic Form Distance for semantic-based detection of near-duplicate video clips | IEEE Conference Publication | IEEE Xplore

Leveraging an image folksonomy and the Signature Quadratic Form Distance for semantic-based detection of near-duplicate video clips


Abstract:

Being able to detect near-duplicate video clips (NDVCs) is a prerequisite for a plethora of multimedia applications. Given the observation that content transformations te...Show More

Abstract:

Being able to detect near-duplicate video clips (NDVCs) is a prerequisite for a plethora of multimedia applications. Given the observation that content transformations tend to preserve semantic information, techniques for NDVC detection may benefit from the use of a semantic approach. This paper discusses how an image folksonomy (i.e., community-contributed images and metadata) and the Signature Quadratic Form Distance (SQFD) can be leveraged for the purpose of identifying NDVCs. Experimental results obtained for the MIRFLICKR-25000 image set and the TRECVID 2009 video set indicate that an image folksonomy and SQFD can be successfully used for detecting NDVCs. In addition, our findings show that model-free NDVC detection (i.e., NDVC detection using an image folksonomy) has a higher semantic coverage than model-based NDVC detection (i.e., NDVC detection using the VIREO-374 semantic concept models).
Date of Conference: 11-15 July 2011
Date Added to IEEE Xplore: 05 September 2011
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Conference Location: Barcelona

References

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