High Definition Visual Attention based Video Summarization

Yiming Qian; Matthew Kyan

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

High Definition Visual Attention based Video Summarization

Topics: Machine Learning Technologies for Vision; Visual Attention and Image Saliency

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, 634-640, 2014 , Lisbon, Portugal

Authors: Yiming Qian and Matthew Kyan

Affiliation: Ryerson University, Canada

Keyword(s): Delta E 2000, Human Vision Model, Self-Organizing Map (SOM), Summarization, Visual Attention.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Visual Attention and Image Saliency

Abstract: A High Definition visual attention based video summarization algorithm is proposed to extract feature frames and create a video summary. It uses colour histogram shot detection algorithm to separate the video into shots, then applies a novel high definition visual attention algorithm to construct a saliency map for each frame. A multivariate mutual information algorithm is applied to select a feature frame to represent each shot. Finally, those feature frames are processed by a self-organizing map to remove the redundant frames. The algorithm was assessed against manual key frame summaries presented with tested datasets from www.open-video.org. Of the frames selected by the algorithm, 27.8% to 68.1% were in agreement with the manual frame summaries depending on the category and length of the video.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 3.236.100.210

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

Qian, Y. and Kyan, M. (2014). High Definition Visual Attention based Video Summarization. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP; ISBN 978-989-758-003-1; ISSN 2184-4321, SciTePress, pages 634-640. DOI: 10.5220/0004742206340640

@conference{visapp14,
author={Yiming Qian. and Matthew Kyan.},
title={High Definition Visual Attention based Video Summarization},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP},
year={2014},
pages={634-640},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004742206340640},
isbn={978-989-758-003-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP
TI - High Definition Visual Attention based Video Summarization
SN - 978-989-758-003-1
IS - 2184-4321
AU - Qian, Y.
AU - Kyan, M.
PY - 2014
SP - 634
EP - 640
DO - 10.5220/0004742206340640
PB - SciTePress