Accurate repeat finding and object skipping using fingerprints
Proceedings of the 13th annual ACM international conference on Multimedia
TV broadcast macro-segmentation: metadata-based vs. content-based approaches
Proceedings of the 6th ACM international conference on Image and video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Mining repetitive clips through finding continuous paths
Proceedings of the 15th international conference on Multimedia
A confidence based recognition system for TV commercial extraction
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Variability Tolerant Audio Motif Discovery
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Less talk, more rock: automated organization of community-contributed collections of concert videos
Proceedings of the 18th international conference on World wide web
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
On the information rates of the plenoptic function
IEEE Transactions on Information Theory
Efficient advertisement discovery for audio podcast content using candidate segmentation
EURASIP Journal on Audio, Speech, and Music Processing
A TV commercial detection system
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
From audio recurrences to TV program structuring
AIEMPro '11 Proceedings of the 2011 ACM international workshop on Automated media analysis and production for novel TV services
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
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Many media streams consist of distinct objects that repeat. For example, broadcast television and radio signals contain advertisements, call sign jingles, songs, and even whole programs that repeat. The problem we address is to explicitly identify the underlying structure in repetitive streams and de-construct them into their component objects. Our algorithm exploits dimension reduction techniques on the audio portion of a multimedia stream to make search and buffering feasible. Our architecture assumes no a priori knowledge of the streams, and does not require that the repeating objects (ROs) be known. Everything the system needs, including the position and duration of the ROs, is learned on the fly. We demonstrate that it is perfectly feasible to identify in realtime ROs that occur days or even weeks apart in audio or video streams. Both the compute and buffering requirements are comfortably within reach for a basic desktop computer. We outline the algorithms, enumerate several applications and present results from real broadcast streams.