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Automatic segmentation of TV news into stories using visual and temporal information

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we propose a new method for automatic storyboard segmentation of TV news using image retrieval techniques and content manipulation. Our framework performs: shot boundary detection, global key-frame representation, image re-ranking based on neighborhood relations and temporal variance of image locations in order to construct a unimodal cluster for anchor person detection and differentiation. Finally, anchor shots are used to form video scenes. The entire technique is unsupervised being able to learn semantic models and extract natural patterns from the current video data. The experimental evaluation performed on a dataset of 50 videos, totalizing more than 30 h, demonstrates the pertinence of the proposed method, with gains in terms of recall and precision rates with more than 5–7% when compared with state of the art techniques.

Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems - 17th International Conference, ACIVS 2016, Proceedings
EditorsCosimo Distante, Dan Popescu, Paul Scheunders, Wilfried Philips, Jacques Blanc-Talon
PublisherSpringer Verlag
Pages648-660
Number of pages13
ISBN (Print)9783319486796
DOIs
Publication statusPublished - 1 Jan 2016
Event17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016 - Lecce, Italy
Duration: 24 Oct 201627 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10016 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016
Country/TerritoryItaly
CityLecce
Period24/10/1627/10/16

Keywords

  • Anchor person extraction
  • News video story segmentation
  • Relevant interest points
  • Temporal and visual constrained clustering

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