LEVERAGING AN IMAGE FOLKSONOMY AND THE SIGNATURE QUADRATIC FORM DISTANCE FOR SEMANTIC-BASED DETECTION OF NEAR-DUPLICATE VIDEO CLIPS
Hyun Seok Min, Jaeyoung Choi, Wesley De Neve, Yong Man RoAbstract
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).
Read Submission [319]