Simultaneous detection of abrupt cuts and dissolves in videos using support vector machines
| dc.contributor.author | Chasanis, V. | en |
| dc.contributor.author | Likas, A. | en |
| dc.contributor.author | Galatsanos, N. | en |
| dc.date.accessioned | 2015-11-24T17:01:56Z | |
| dc.date.available | 2015-11-24T17:01:56Z | |
| dc.identifier.issn | 0167-8655 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11001 | |
| dc.rights | Default Licence | - |
| dc.subject | abrupt cut detection | en |
| dc.subject | dissolve detection | en |
| dc.subject | support vector machines | en |
| dc.subject | shot-boundary detection | en |
| dc.title | Simultaneous detection of abrupt cuts and dissolves in videos using support vector machines | en |
| heal.abstract | Video shot detection is an important contemporary problem since it is the first step towards indexing and content based video retrieval. Traditionally, video shot segmentation approaches rely on thresholding methodologies which are sensitive to the content of the video being processed and do not generalize well the when there is little prior knowledge about the video content. To ameliorate this shortcoming we propose a learning based methodology using a set of features that are specifically designed to capture the differences among hard cuts, gradual transitions and normal sequences of frames at the same time. A support vector machine (SVM) classifier is trained both to locate shot boundaries and characterize transition types. Numerical experiments using a variety of videos demonstrate that our method is capable of accurately discriminating shot transitions in videos with different characteristics.(C) 2008 Elsevier B.V. All rights reserved. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | DOI 10.1016/j.patrec.2008.08.015 | - |
| heal.journalName | Pattern Recognition Letters | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2009 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
Αρχεία
Φάκελος/Πακέτο αδειών
1 - 1 of 1
Φόρτωση...
- Ονομα:
- license.txt
- Μέγεθος:
- 1.74 KB
- Μορφότυπο:
- Item-specific license agreed upon to submission
- Περιγραφή: