Publication
Title
Video summarization based on Subclass Support Vector Data Description
Author
Abstract
In this paper, we describe a method for video summarization that operates on a video segment level. We formulate this problem as the one of automatic video segment selection based on a learning process that employs salient video segment paradigms. We design an hierarchical learning scheme that consists of two steps. At the first step, an unsupervised process is performed in order to determine salient video segment types. The second step is a supervised learning process that is performed for each of the salient video segment type independently. For the latter case, since only salient training examples are available, the problem is stated as an one-class classification problem. In order to take into account subclass information that may appear in the video segment types, we introduce a novel formulation of the Support Vector Data Description method that exploits subclass information in its optimization process. We evaluate the proposed approach in three Hollywood movies, where the performance of the proposed Subclass SVDD (SSVDD) algorithm is compared with that of related methods. Experimental results show that the adoption of both hierarchical learning and the proposed SSVDD method contribute to the final classification performance.
Language
English
Source (book)
2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES), 09-12 December, 2014, Orlando, FL, USA
Publication
New york : 2014
ISBN
978-1-4799-4509-2
DOI
10.1109/CIES.2014.7011849
Volume/pages
(2014) , p. 183-187
ISI
000380441500027
Full text (Publisher's DOI)
UAntwerpen
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 17.10.2023
Last edited 20.08.2024
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