Big Visual Data Analysis

Scene Classification and Geometric Labeling

  • Chen Chen
  • Yuzhuo Ren
  • C.-C. Jay Kuo

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Also part of the SpringerBriefs in Signal Processing book sub series (BRIEFSSIGNAL)

Table of contents

  1. Front Matter
    Pages i-x
  2. Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 1-5
  3. Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 7-21
  4. Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 23-63
  5. Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 65-92
  6. Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 93-120
  7. Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 121-122

About this book


This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.


Big Visual Data Analysis Scene Understanding Indoor/Outdoor Classification Outdoor Scene Classification Outdoor Scene Geometric Labeling

Authors and affiliations

  • Chen Chen
    • 1
  • Yuzhuo Ren
    • 2
  • C.-C. Jay Kuo
    • 3
  1. 1.Dept. of Electrical EngineeringUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Univ of Southern CaliforniaDept of Electrical Engineerin,Apt. #7Los AngelesUSA
  3. 3.University of Southern CaliforniaLos AngelesUSA

Bibliographic information

  • DOI
  • Copyright Information The Author(s) 2016
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-981-10-0629-6
  • Online ISBN 978-981-10-0631-9
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • Buy this book on publisher's site
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