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 contributionScientificpeer-review

    10 Citations (Scopus)

    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 publicationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIES 2014: 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, Proceedings
    PublisherIEEE
    Pages183-187
    Number of pages5
    ISBN (Print)9781479945108
    DOIs
    Publication statusPublished - 15 Jan 2015
    Publication typeA4 Article in conference proceedings
    Event2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2014 - Orlando, United States
    Duration: 9 Dec 201412 Dec 2014

    Conference

    Conference2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2014
    Country/TerritoryUnited States
    CityOrlando
    Period9/12/1412/12/14

    ASJC Scopus subject areas

    • Computer Science Applications
    • Artificial Intelligence
    • Computational Theory and Mathematics

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