Conference Paper

Repetition density-based approach for TV program extraction.

DOI: 10.1109/WIAMIS.2009.5031463 Conference: 10th Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2009, London, United Kingdom, May 6-8, 2009
Source: DBLP


This paper addresses the problem of automatic TV broad- casted program extraction. It consists firstly of precisely de- termining the start and the end of each broadcasted TV pro- gram, and then of properly giving them a name. The extracted programs can be used to build novel services like TV-on- Demand. The proposed solution is based on the density study of repeated audiovisual sequences. This study allows to sort out most of the inter-programs from the repeated sequences. The effectiveness of our solution has been shown on two dis- tinct real TV streams lasting 5 days. A comparative eval- uation with traditional approaches has also been performed (metadata-based and silences-and-monochrome-frames-based).

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Available from: Sid Ahmed Berrani, Jun 22, 2014
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    ABSTRACT: This paper addresses the problem of automatic broadcasted TV program extraction from the low-level video data without using any metadata. In this context, the TV stream is first segmented. Segments are then classified into two categories: segments of inter-programs (e.g. commercials) and segments of programs that are parts of broadcasted TV programs (e.g. films, news, shows). One TV program can hence be split into several parts over a set of consecutive program segments. Consecutive program segments of the same TV program thus have to be reunified or fused in order to retrieve the entire TV program. This consecutive program segment reunification is the main concern of the paper. We focus in particular on the case where no metadata is available. We assume that the different parts of a same TV program share a set of features. Hence, our solution relies on analyzing the visual content and characteristics of each pair of consecutive segments in order to decide if they have to be reunified or not. It uses, amongst others, content-based descriptors like the color distribution, the number of faces in each segment and also the number of near-identical shots between the two segments. These descriptors are then used within an SVM classifier which makes the final decision. The effectiveness of the solution has been shown experimentally using a real TV stream of three weeks.
    Full-text · Conference Paper · Jan 2009
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    ABSTRACT: Using specifically-designed lightweight audio and video fingerprints, we were able to detect repeated contents over a quasi-uninterrupted recording of 10+ TV channels, over more than 4 years, starting January 2010 (380,000 hours); the detection independently uses audio and video fingerprints. The results are stored into a database that holds more than 20 million detected repeats. Detections range from a few seconds up to one hour. The database can be explored using a standard web browser. There are a many potential applications, e.g. for structuring and documenting contents.
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