Video summarization based on Subclass Support Vector Data Description

Vasileios Mygdalis, Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

219 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES 2014)
Subtitle of host publicationProceedings of a meeting held 9-12 December 2014, Orlando, Florida, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages183-187
Number of pages5
ISBN (Electronic)9781479945092
ISBN (Print)9781479945085
DOIs
Publication statusPublished - Mar 2015
Event2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) - Orlando, FL, United States
Duration: 9 Dec 201412 Dec 2014

Conference

Conference2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)
CountryUnited States
CityOrlando, FL
Period9/12/1412/12/14

Fingerprint Dive into the research topics of 'Video summarization based on Subclass Support Vector Data Description'. Together they form a unique fingerprint.

Cite this