The COST292 experimental framework for rushes summarization task in TRECVID 2008

Naci, Suphi Umut; Damnjanovic, Uros; Mansencal, Boris; Benois-Pineau, Jenny; Kaes, Christian; Corvaglia, Marzia; Rossi, Eliana; Aginako, Naiara

doi:10.1145/1463563.1463569

Cited by 8 publications

(9 citation statements)

References 4 publications

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“…In the algorithm, faces are the primary targets, as they constitute the focus of most consumer video programs. Naci et al [277] extract features using face detection, camera motion, and MPEG-7 color layout descriptors of each frame. A clustering algorithm is employed to find and then remove repeated shots.…”

Section: ) Redundancy Removalmentioning

confidence: 99%

A Survey on Visual Content-Based Video Indexing and Retrieval

Xie

et al. 2011

IEEE Trans. Syst., Man, Cybern. C

469

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Abstract-Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.Index Terms-Feature extraction, video annotation, video browsing, video retrieval, video structure analysis.

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Section: ) Redundancy Removalmentioning

confidence: 99%

A Survey on Visual Content-Based Video Indexing and Retrieval

Xie

et al. 2011

IEEE Trans. Syst., Man, Cybern. C

469

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show abstract

“…Indexing of video shots according to the associated textual information is realized following the approach of [27]. The audio information is processed off line with the application of automatic speech recognition (ASR) on the initial video source, so that specific sets of keywords can be assigned to each shot.…”

Section: Keyword Indexingmentioning

confidence: 99%

“…The visual similarity shot indexing is realized with the extraction of low level visual descriptors following the approach of [27]. In this case, five MPEG-7 descriptors, namely, color layout, color structure, scalable color, edge histogram, homogeneous texture, are generated from the representative keyframe of each video shot and stored in a relational database.…”

Section: Visual Similarity Indexingmentioning

confidence: 99%

“…By concatenating these descriptors, a feature vector is formulated to compactly represent each keyframe in the multidimensional space. An empirical evaluation of the system's performance using different combinations of the aforementioned descriptors advocated the choice of one MPEG-7-based scheme, which relies on color and texture (i.e., color layout and edge histogram are concatenated) [27]. Distance calculation between the descriptors of two shots is performed by employing the functions proposed in the MPEG eXperimentation Model [31].…”

Section: Visual Similarity Indexingmentioning

confidence: 99%

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Utilizing Implicit User Feedback to Improve Interactive Video Retrieval

Vrochidis

Kompatsiaris

Patras

2011

Advances in Multimedia

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This paper describes an approach to exploit the implicit user feedback gathered during interactive video retrieval tasks. We propose a framework, where the video is first indexed according to temporal, textual, and visual features and then implicit user feedback analysis is realized using a graph-based methodology. The generated graph encodes the semantic relations between video segments based on past user interaction and is subsequently used to generate recommendations. Moreover, we combine the visual features and implicit feedback information by training a support vector machine classifier with examples generated from the aforementioned graph in order to optimize the query by visual example search. The proposed framework is evaluated by conducting real-user experiments. The results demonstrate that significant improvement in terms of precision and recall is reported after the exploitation of implicit user feedback, while an improved ranking is presented in most of the evaluated queries by visual example.

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“…The COST Action 292 Group is a large consortium of European research partners from the Netherlands, UK, France, Italy and Spain and extended their 2007 summarization participation by developing new approaches to detecting repetition [18]. As with most other groups, junk frames were explicitly detected and removed and the unit of manipulation was the scene rather than the shot or frame.…”

Section: Participants and Their Approachesmentioning

confidence: 99%

The trecvid 2008 BBC rushes summarization evaluation

Over

Smeaton

Awad

2008

Proceedings of the 2nd ACM TRECVid Video Summarization Workshop

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This paper describes an evaluation of automatic video summarization systems run on rushes from several BBC dramatic series. It was carried out under the auspices of the TREC Video Retrieval Evaluation (TRECVid) as a followup to the 2007 video summarization workshop held at ACM Multimedia 2007. 31 research teams submitted video summaries of 40 individual rushes video files, aiming to compress out redundant and insignificant material. Each summary had a duration of at most 2% of the original. The output of a baseline system, which simply presented each full video at 50 times normal speed was contributed by Carnegie Mellon University (CMU) as a control.The 2007 procedures for developing ground truth lists of important segments from each video were applied at the National Institute of Standards and Technology (NIST) to the BBC videos. At Dublin City University (DCU) each summary was judged by 3 humans with respect to how much of the ground truth was included and how well-formed the summary was. Additional objective measures included: how long it took the system to create the summary, how long it took the assessor to judge it against the ground truth, and what the summary's duration was. Assessor agreement on finding desired segments averaged 81%. Results indicated that while it was still difficult to exceed the performance of the baseline on in-1 cluding ground truth, the baseline was outperformed by most other systems with respect to avoiding redundancy/junk and presenting the summary with a pleasant tempo/rhythm.

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The COST292 experimental framework for rushes summarization task in TRECVID 2008

Cited by 8 publications

References 4 publications

A Survey on Visual Content-Based Video Indexing and Retrieval

A Survey on Visual Content-Based Video Indexing and Retrieval

Utilizing Implicit User Feedback to Improve Interactive Video Retrieval

The trecvid 2008 BBC rushes summarization evaluation

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