Hierarchical modeling and adaptive clustering for real-time summarization of rush videos in trecvid'08

Ren, Jinchang; Jiang, Jianmin; Eckes, Christian

doi:10.1145/1463563.1463566

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…A description of such sports video databases is highlighted in Table 2, where a total of 1592 shots, lasting around 5 to 30 s each, are extracted from all the videos, 17 and the frame size is 352×240. To measure the performances of the proposed SVM tree, we adopt the widely known precision and recall rates for presenting our experimental results.…”

Section: Resultsmentioning

confidence: 99%

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

et al. 2010

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Abstract. We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications. C 2010 Society of Photo-Optical Instrumentation Engineers.Optical Engineering 49(12), 127003 (December 2010

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Section: Resultsmentioning

confidence: 99%

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

et al. 2010

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“…Future work will be in several ways. One is video analysis before transcoding and transmission, including video segmentation and content extraction [19][20][21] as well as denoising and decomposition [22][23][24]. In addition, object-based analysis with most state-of-the-art machine learning approaches will be highlighted as well, using deep learning and weakly-supervised learning [25][26][27].…”

Section: Discussionmentioning

confidence: 99%

A Secure Communication Model with Compressive-Sensed Multimedia Data

Zhao¹,

Zhang²,

Dai³

et al. 2017

IJFGCN

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“…The University of Bradford in the UK, working with the Fraunhofer Institute in Germany [22] sought to model rushes as an hierarchical structure and to exploit this structure in deciding what to include in the summary. A k-NN clustering approach was used based on visual similarity between shot keyframes.…”

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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Hierarchical modeling and adaptive clustering for real-time summarization of rush videos in trecvid'08

Cited by 12 publications

References 11 publications

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

A Secure Communication Model with Compressive-Sensed Multimedia Data

The trecvid 2008 BBC rushes summarization evaluation

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