Discriminative learning- assisted video semantic concept classification

Qiusha Zhu, Mei Ling Shyu, Shu Ching Chen

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

With the fast development and wide usage of digital devices, more and more people like to record their daily lives as videos and share them on websites such as YouTube, Yahoo Video, and Youku, to name a few. To get a better understanding of such video data and discover useful knowledge from them, effective and automatic multimedia semantic analysis becomes crucial. However, a manual analysis of multimedia data can be very expensive or simply not feasible when the time is limited or when the amount of data is enormous. Hence, the multimedia research community faces a major challenge: how to effectively and efficiently organize these videos. Data mining techniques have been successfully utilized to provide solutions to such a challenge. An example is video concept classification, which has been an attractive research focus in the past 10 years (Chen et al., 2006a; Shyu et al., 2008).

Original languageEnglish (US)
Title of host publicationMultimedia Security
Subtitle of host publicationWatermarking, Steganography, and Forensics
PublisherCRC Press
Pages31-49
Number of pages19
ISBN (Electronic)9781439873328
ISBN (Print)9781439873311
DOIs
StatePublished - Jan 1 2017

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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  • Cite this

    Zhu, Q., Shyu, M. L., & Chen, S. C. (2017). Discriminative learning- assisted video semantic concept classification. In Multimedia Security: Watermarking, Steganography, and Forensics (pp. 31-49). CRC Press. https://doi.org/10.1201/b12697