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Limited By:	Subject = TRECVID;

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Displaying Results 1 - 4 of 4 on page 1 of 1

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A user-centered approach to rushes summarisation via highlight-detected keyframes (2007)

Byrne, Daragh; Kehoe, Peter; Lee, Hyowon; Ó Conaire, Ciarán; Smeaton, Alan F.; O'C...

A user-centered approach to rushes summarisation via highlight-detected keyframes (2007)

Byrne, Daragh; Kehoe, Peter; Lee, Hyowon; Ó Conaire, Ciarán; Smeaton, Alan F.; O'Connor, Noel E.; Jones, Gareth J.F.

Abstract:

We present our keyframe-based summary approach for BBC Rushes video as part of the TRECVid Summarisation benchmark evaluation carried out in 2007. We outline our approach to summarisation that uses video processing for feature extraction and is informed by human factors considerations for summary presentation. Based on the performance of our generated summaries as reported by NIST, we subsequently undertook detailed failure analysis of our approach. The findings of this investigation as well as recommendations for alterations to our keyframe-based summary generation method, and the evaluation methodology for Rushes summaries in general, are detailed within this paper.

http://doras.dcu.ie/409/

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Aggregated Feature Retrieval for MPEG-7 via Clustering (2004)

Ye, Jiamin; Smeaton, Alan F.

Aggregated Feature Retrieval for MPEG-7 via Clustering (2004)

Ye, Jiamin; Smeaton, Alan F.

Abstract:

In this paper, we describe an approach to combining text and visual features from MPEG-7 descriptions of video. A video retrieval process is aligned to a text retrieval process based on the TF*IDF vector space model via clustering of low-level visual features. Our assumption is that shots within the same cluster are not only similar visually but also semantically, to a certain extent. Our experiments on the TRECVID2002 and TRECVID2003 collections show that adding extra meaning to a shot based on the shots from the same cluster is useful when each video in a collection contains a high proportion of similar shots, for example in documentaries.

http://doras.dcu.ie/374/

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Inexpensive fusion methods for enhancing feature detection (2007)

Wilkins, Peter; Adamek, Tomasz; O'Connor, Noel E.; Smeaton, Alan F.

Inexpensive fusion methods for enhancing feature detection (2007)

Wilkins, Peter; Adamek, Tomasz; O'Connor, Noel E.; Smeaton, Alan F.

Abstract:

Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused together in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated exp...

http://doras.dcu.ie/209/

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Rushes video summarization using a collaborative approach (2008)

Dumont, Emilie; Merialdo, Bernard; Essid, Slim; Bailer, Werner; Rehatschek, Herwig; Byr...

Rushes video summarization using a collaborative approach (2008)

Dumont, Emilie; Merialdo, Bernard; Essid, Slim; Bailer, Werner; Rehatschek, Herwig; Byrne, Daragh; Bredin, Hervé; O'Connor, Noel E.; Jones, Gareth J.F.; Smeaton, Alan F.; Haller, Martin; Krutz, Andreas; Sikora, Thomas; Piatrik, Tomas

Abstract:

This paper describes the video summarization system developed by the partners of the K-Space European Network of Excellence for the TRECVID 2008 BBC rushes summarization evaluation. We propose an original method based on individual content segmentation and selection tools in a collaborative system. Our system is organized in several steps. First, we segment the video, secondly we identify relevant and redundant segments, and finally, we select a subset of segments to concatenate and build the final summary with video acceleration incorporated. We analyze the performance of our system through the TRECVID evaluation.

http://doras.dcu.ie/16186/

Displaying Results 1 - 4 of 4 on page 1 of 1

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2008 (1)
2007 (2)
2004 (1)

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