Efficient use of local edge histogram descriptor
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Finding Periodicity in Space and Time
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
(Un)Reliability of video concept detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
IEEE Transactions on Circuits and Systems for Video Technology
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Concept-based video retrieval is a developing area of current multimedia content analysis research. The use of spatio-temporal descriptors in content-based video retrieval has always seemed like a promising way to bridge the semantic gap problem in ways that typical visual retrieval methods cannot. In this paper we propose a spatio-temporal descriptor called ST-MP7EH which can address some of the challenges encountered in practical systems and we present our experimental results in support of our participation at TRECVid 2011 Semantic Indexing. This descriptor combines the MPEG-7 Edge Histogram descriptor with motion information and is designed to be computationally efficient, scalable and highly parallel. We show that our descriptor performs well in SVM classification compared to a baseline spatio-temporal descriptor, which is inspired by some of the state-of-the-art systems that make the top lists of TRECVid. We highlight the importance of the temporal component by comparing to the initial edge histogram descriptor and the potential of feature fusion with other classifiers.