Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Real time repeated video sequence identification
Computer Vision and Image Understanding
Finding and identifying unknown commercials using repeated video sequence detection
Computer Vision and Image Understanding
Mining repetitive clips through finding continuous paths
Proceedings of the 15th international conference on Multimedia
Detecting repeats for video structuring
Multimedia Tools and Applications
Evaluation of GIST descriptors for web-scale image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Mining TV broadcasts for recurring video sequences
Proceedings of the ACM International Conference on Image and Video Retrieval
Fast structuring of large television streams using program guides
AMR'06 Proceedings of the 4th international conference on Adaptive multimedia retrieval: user, context, and feedback
Locality sensitive hashing: A comparison of hash function types and querying mechanisms
Pattern Recognition Letters
Product Quantization for Nearest Neighbor Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient k-nearest neighbor graph construction for generic similarity measures
Proceedings of the 20th international conference on World wide web
Efficient Short Video Repeat Identification With Application to News Video Structure Analysis
IEEE Transactions on Multimedia
Scalable k-NN graph construction for visual descriptors
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
Hi-index | 0.00 |
Duplicates or near-duplicates mining in video sequences is of broad interest to many multimedia applications. How to design an effective and scalable system, however, is still a challenge to the community. In this paper, we present a method to detect recurrent sequences in large-scale TV streams in an unsupervised manner and with little a priori knowledge on the content. The method relies on a product k-means quantizer that efficiently produces hash keys adapted to the data distribution for frame descriptors. This hashing technique combined with a temporal consistency check allows the detection of meaningful repetitions in TV streams. When considering all frames (about 47 millions) of a 22-day long TV broadcast, our system detects all repetitions in 15 minutes, excluding the computation of the frame descriptors. Experimental results show that our approach is a promising way to deal with very large video databases.