Dialogue Scenes Detection in MPEG Movies: A Multi-expert Approach

Santo, Massimo De; Percannella, Gennaro; Sansone, Carlo; Vento, Mario

doi:10.1007/3-540-44819-5_16

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…De Santo et al applied multiple experts for dialogue/non-dialogue detection [2]. The applied database consisted of movie audio and video tracks.…”

Section: E Discussionmentioning

confidence: 99%

“…Movie dialogue detection is a challenging problem within movie event analysis, since there are no limitations on the emotional state of persons, the rate at which scenes interchange, the duration of silent periods, and the volume of background noise or music. For example, the detection of dialogue scenes in a movie is more complicated than detecting changes between anchor persons in TV-news, since many different scene types are incorporated in movies depending on the movie director [2]. Dialogue detection in conjunction with face and/or speaker identification could locate the scenes, where two or more particular persons are conversing.…”

Section: Introductionmentioning

confidence: 99%

“…In [2], a multi-expert system performs dialogue detection. Three experts are employed, namely face detection, cameramotion estimation, and audio classification.…”

Section: Introductionmentioning

confidence: 99%

“…The most common segmentation is into speech, music, and silence [1], [9]. More complicated cases include speech, music, silence, music, noise, speech with music, speech with noise, and music with noise [2] or in speech mixed with music, speech mixed with environmental background sound, and environment sound mixed with music [3]. Dialogue occurs if there is pure speech or mixed speech in a scene [6].…”

Section: Introductionmentioning

confidence: 99%

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Audio-Assisted Movie Dialogue Detection

Kotti

Ververidis

Evangelopoulos

et al. 2008

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the crosscorrelation and the magnitude of the corresponding the crosspower spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptrons, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported.

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“…De Santo et al applied multiple experts for dialogue/non-dialogue detection [2]. The applied database consisted of movie audio and video tracks.…”

Section: E Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In [2], a multi-expert system performs dialogue detection. Three experts are employed, namely face detection, cameramotion estimation, and audio classification.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Audio-Assisted Movie Dialogue Detection

Kotti

Ververidis

Evangelopoulos

et al. 2008

IEEE Trans. Circuits Syst. Video Technol.

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“…Consequently, in order to test an algorithm, it is crucial to define a specific model that avails clear criteria in determining a scene. Several papers try to define models for scene detection, mainly in the field of TV-news, where simple and effective models can be defined [8]. News headings, graphics of the station's logo, anchorperson shots and prerecorded news videos are some of the most common scenes set for news shows.…”

Section: Modelling the Problemmentioning

confidence: 99%

Audio PCA in a novel multimedia scheme for scene change detection

Kyperountas

Cernekova

Kotropoulos

et al.

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing

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A novel scene change detection algorithm is proposed in this paper that exploits both audio and video information. Audio frames are projected to the eigenspace and their distance from a reference noise eigenframe is calculated. An analysis is presented that explains why this subspace favors scene cut detection. Video information is used to align audio scene change indications with neighboring shot changes in the visual data, and accordingly to reduce the false alarm rate. Moreover, video fade effects are identified and used independently in order to track scene changes. The detection technique was tested on newscast videos provided by the TRECVID 2003 video test set [1]. The experimental results show that the aforementioned methods used to process the audio and video information complement each other well in tackling the scene change detection problem.

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