Automatic TV Broadcast Structuring

Manson, Gaël; Berrani, Sid-Ahmed

doi:10.1155/2010/153160

Cited by 12 publications

(22 citation statements)

References 7 publications

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“…As a result, we obtain the shape descriptor s in Equation (16), which contains a total of 7 elements:…”

Section: Figurementioning

confidence: 99%

“…[15][16][17][18] The information content extracted from video analysis and abstraction processes are usually referred to as metadata. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Experiments were performed on datasets of a popular TV show, demonstrating the accuracy of the proposed solution. Manson & Berrani [16] present an automatic system, named DealTV, for TV broadcast structuring. The proposed system performs the segmentation of the TV stream and classifies the segments by using an inductive logic programming-based technique.…”

Section: Introductionmentioning

confidence: 99%

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A unified approach to content-based indexing and retrieval of digital videos from television archives

Souza¹,

Pádua

Nunes³

et al. 2014

AIR

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This work addresses the development of a unified approach to content-based indexing and retrieval of digital videos from television archives. The proposed approach has been designed to deal with arbitrary television genres, making it suitable for various applications. To achieve this goal, the main steps of a content-based video retrieval system are addressed in this work, namely: video segmentation, key-frame extraction, content-based video indexing and the video retrieval operation itself. Video segmentation is addressed as a typical TV broadcast structuring problem, which consists in automatically determining the boundaries of each broadcasted program (like movies, news, among others) and inter-program (for instance, commercials). Specifically, to segment the videos, Electronic Program Guide (EPG) metadata is combined with the detection of two special cues, namely, audio cuts (silence) and dark monochrome frames. On the other hand, a color histogram-based approach performs key-frame extraction. Video indexing and retrieval are accomplished by using hashing and k-d tree methods, while visual signatures containing color, shape and texture information are estimated for the key-frames, by using image and frequency domain techniques. Experimental results with the dataset of a multimedia information system especially developed for managing television broadcast archives demonstrate that our approach works efficiently, retrieving videos in 0.16 seconds on average and achieving recall, precision and F1 measure values, as high as 0.76, 0.97 and 0.86 respectively.

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“…As a result, we obtain the shape descriptor s in Equation (16), which contains a total of 7 elements:…”

Section: Figurementioning

confidence: 99%

“…[15][16][17][18] The information content extracted from video analysis and abstraction processes are usually referred to as metadata. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A unified approach to content-based indexing and retrieval of digital videos from television archives

Souza¹,

Pádua

Nunes³

et al. 2014

AIR

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“…(i) Segmenting the stream into logical units and then classifying each segment as a program or a break segment [2]. These segments may be of different granularities (Key-frame, Shot, Scene,.…”

Section: State Of the Artmentioning

confidence: 99%

“…Nevertheless, this approach requires a lot of manual annotation and cannot scale up. On the other hand, Manson and Berrani [2] proposed a new technique based on a supervised learning algorithm, but this technique also requires manual annotation in order to train the system. Moreover, Manson and et al classify each segment independently from its repetitions even if they probably have the same content type (P or B).…”

Section: Introductionmentioning

confidence: 99%

TV Stream Structuring

Ibrahim

Gros

2011

ISRN Signal Processing

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TV stream structuring consists in detecting precisely the first and the last frames of all the programs and the breaks (commercials, trailers, station identification, bumpers) of a given stream and then in annotating all these segments with metadata. Usually, breaks are broadcasted several times during a stream. Thus, the detection of these repetitions can be considered as a key tool for stream structuring. After the detection stage, a classification method is applied to separate the repetitions in programs and breaks. In their turn, breaks repetitions are then used to classify the segments which appear only once in the stream. Finally, the stream is aligned with an electronic program guide (EPG), in order to annotate the programs. Our experiments have been applied on a 22-day long TV stream, and results show the efficiency of the proposed method in TV stream structuring.

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