Repository logo
  • English
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
Repository logo
  • Communities & Collections
  • Research Outputs
  • Projects
  • People
  • Statistics
  • English
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Scholalry Output
  3. Publications
  4. A review on video summarization techniques
 
  • Details
Options

A review on video summarization techniques

ISSN
09521976
Date Issued
2023-02-01
Author(s)
Meena, Preeti
Kumar, Himanshu
Kumar Yadav, Sandeep
DOI
10.1016/j.engappai.2022.105667
Abstract
The exponential growth of technology has resulted in a profusion of advanced imaging devices and eases internet accessibility, leading to an increase in the creation and use of multimedia content. Analyzing representative or meaningful information from such massive data is a time-consuming task that impacts the efficiency of various video processing applications, including video searching, retrieval, indexing, sharing, and many more. In literature, numerous video summarization techniques which extract key-frames or key-shots from the original video to generate a concise yet informative summary have been proposed to address these issues. This paper presents a discussion of the state-of-the-art video summarization techniques along with limitations and challenges. The paper examines summarization techniques in a holistic manner based upon the distinct attributes of evolving video data types on the basis of parameters such as the number of views, dimensions, modality, and content. Such a categorization framework enables us to critically analyze the recent progress, future directions, limitations, datasets, application domains etc., in a better comprehensible manner.
Subjects
  • Modality fusion

  • Multi-modal

  • Multi-view

  • Single view

  • Video summarization

Copyright © 2016-2025  Indian Institute of Technology Jodhpur

Developed and Maintaining by S. R. Ranganathan Learning Hub, IIT Jodhpur.

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback