Temporal video segmentation for real-time key frame extraction

Calic, Janko; Sav, Sorin; Izquierdo, Ebroul; Marlow, Seán; Murphy, Noel; O'Connor, N.

doi:10.1109/icassp.2002.5745442

Cited by 6 publications

(1 citation statement)

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“…The different algorithms for segmentation of the video are studied. Temporal video segmentation for real-time key frame extraction is discussed (Calic, J 1993). A temporal video segmentation method proposed is based on the detection of shot abrupt transition and gradual transition, and then takes into account the conditions of user terminals, which could generate different video summarization for each user (Chen Yinzi 2010).…”

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confidence: 99%

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2016

IJCSA

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confidence: 99%

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2016

IJCSA

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Overview of VideoCLEF 2008: Automatic Generation of Topic-Based Feeds for Dual Language Audio-Visual Content

Larson

Newman

Jones

2009

Lecture Notes in Computer Science

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The VideoCLEF track, introduced in 2008, aims to develop and evaluate tasks related to analysis of and access to multilingual multimedia content. In its first year, VideoCLEF piloted the Vid2RSS task, whose main subtask was the classification of dual language video (Dutchlanguage television content featuring English-speaking experts and studio guests). The task offered two additional discretionary subtasks: feed translation and automatic keyframe extraction. Task participants were supplied with Dutch archival metadata, Dutch speech transcripts, English speech transcripts and 10 thematic category labels, which they were required to assign to the test set videos. The videos were grouped by class label into topic-based RSS-feeds, displaying title, description and keyframe for each video.Five groups participated in the 2008 VideoCLEF track. Participants were required to collect their own training data; both Wikipedia and general web content were used. Groups deployed various classifiers (SVM, Naive Bayes and k-NN) or treated the problem as an information retrieval task. Both the Dutch speech transcripts and the archival metadata performed well as sources of indexing features, but no group succeeded in exploiting combinations of feature sources to significantly enhance performance. A small scale fluency/adequacy evaluation of the translation task output revealed the translation to be of sufficient quality to make it valuable to a non-Dutch speaking English speaker. For keyframe extraction, the strategy chosen was to select the keyframe from the shot with the most representative speech transcript content. The automatically selected shots were shown, with a small user study, to be competitive with manually selected shots. Future years of VideoCLEF will aim to expand the corpus and the class label list, as well as to extend the track to additional tasks.

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Multimodal indexing of digital audio-visual documents: A case study for cultural heritage data

Carmichael

Larson

Marlow

et al. 2008

2008 International Workshop on Content-Based Multimedia Indexing

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This paper describes a multimedia multimodal information access sub-system (MIAS) for digital audio-visual documents, typically presented in streaming media format. The system is designed to provide both professional and general users with entry points into video documents that are relevant to their information needs. In this work, we focus on the information needs of multimedia specialists at a Dutch cultural heritage institution with a large multimedia archive. A quantitative and qualitative assessment is made of the efficiency of search operations using our multimodal system and it is demonstrated that MIAS significantly facilitates information retrieval operations when searching within a video document.

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Temporal video segmentation for real-time key frame extraction

Cited by 6 publications

References 15 publications

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Overview of VideoCLEF 2008: Automatic Generation of Topic-Based Feeds for Dual Language Audio-Visual Content

Multimodal indexing of digital audio-visual documents: A case study for cultural heritage data

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