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Detection of video sequences using compact signatures
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Local Behaviours Labelling for Content Based Video Copy Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
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Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Near-duplicate keyframe retrieval with visual keywords and semantic context
Proceedings of the 6th ACM international conference on Image and video retrieval
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Proceedings of the 6th ACM international conference on Image and video retrieval
Practical elimination of near-duplicates from web video search
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Content based video matching using spatiotemporal volumes
Computer Vision and Image Understanding
(Un)Reliability of video concept detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Video sequence matching based on temporal ordinal measurement
Pattern Recognition Letters
Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Scalable mining of large video databases using copy detection
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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Image Communication
A compact, effective descriptor for video copy detection
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IEEE Transactions on Multimedia
Near-Duplicate Video Detection Using Temporal Patterns of Semantic Concepts
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
Video identification using video tomography
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Scaling content-based video copy detection to very large databases
Multimedia Tools and Applications
Detecting duplicate video based on camera transitional behavior
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Looking at near-duplicate videos from a human-centric perspective
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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IEEE Transactions on Multimedia
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IEEE Transactions on Multimedia
Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning
IEEE Transactions on Multimedia
On the Annotation of Web Videos by Efficient Near-Duplicate Search
IEEE Transactions on Multimedia
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Spatiotemporal sequence matching for efficient video copy detection
IEEE Transactions on Circuits and Systems for Video Technology
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IEEE Transactions on Circuits and Systems for Video Technology
A Framework for Handling Spatiotemporal Variations in Video Copy Detection
IEEE Transactions on Circuits and Systems for Video Technology
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
What fresh media are you looking for?: retrieving media items from multiple social networks
Proceedings of the 2012 international workshop on Socially-aware multimedia
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The detection of near-duplicate video clips (NDVCs) is an area of current research interest and intense development. Most NDVC detection methods represent video clips with a unique set of low-level visual features, typically describing color or texture information. However, low-level visual features are sensitive to transformations of the video content. Given the observation that transformations tend to preserve the semantic information conveyed by the video content, we propose a novel approach for identifying NDVCs, making use of both low-level visual features (this is, MPEG-7 visual features) and high-level semantic features (this is, 32 semantic concepts detected using trained classifiers). Experimental results obtained for the publicly available MUSCLE-VCD-2007 and TRECVID 2008 video sets show that bimodal fusion of visual and semantic features facilitates robust NDVC detection. In particular, the proposed method is able to identify NDVCs with a low missed detection rate (3% on average) and a low false alarm rate (2% on average). In addition, the combined use of visual and semantic features outperforms the separate use of either of them in terms of NDVC detection effectiveness. Further, we demonstrate that the effectiveness of the proposed method is on par with or better than the effectiveness of three state-of-the-art NDVC detection methods either making use of temporal ordinal measurement, features computed using the Scale-Invariant Feature Transform (SIFT), or bag-of-visual-words (BoVW). We also show that the influence of the effectiveness of semantic concept detection on the effectiveness of NDVC detection is limited, as long as the mean average precision (MAP) of the semantic concept detectors used is higher than 0.3. Finally, we illustrate that the computational complexity of our NDVC detection method is competitive with the computational complexity of the three aforementioned NDVC detection methods.