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Big Visual Data Analysis

Scene Classification and Geometric Labeling

  • Book
  • © 2016

Overview

Authors:
  1. Chen Chen

    1. Dept. of Electrical Engineering, University of Southern California, Los Angeles, USA

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  2. Yuzhuo Ren

    1. Univ of Southern California, Dept of Electrical Engineerin,Apt. #7, Los Angeles, USA

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    You can also search for this author in PubMed   Google Scholar

  3. C.-C. Jay Kuo

    1. University of Southern California, Los Angeles, USA

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  • Presents a comprehensive big visual data analysis methodology
  • that helps readers understand the topic quickly and fully
  • Includes abundant insightful data analysis results and comparisons that can be
  • used for other related computer-vision tasks such as scene analysis and image comprehension
  • Provides source codes of data analysis applications (indoor/outdoor classification and vanishing point detection) so that readers can test the algorithms and develop more advanced applications
  • Written by leading experts in the field
  • Includes supplementary material: sn.pub/extras

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

Part of the book sub series: SpringerBriefs in Signal Processing (BRIEFSSIGNAL)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Introduction

    • Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 1-5

  3. Scene Understanding Datasets

    • Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 7-21

  4. Indoor/Outdoor Classification with Multiple Experts

    • Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 23-63

  5. Outdoor Scene Classification Using Labeled Segments

    • Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 65-92

  6. Global-Attributes Assisted Outdoor Scene Geometric Labeling

    • Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 93-120

  7. Conclusion and Future Work

    • Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo
    Pages 121-122
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Keywords

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.

Authors and Affiliations

  • Dept. of Electrical Engineering, University of Southern California, Los Angeles, USA

    Chen Chen

  • Univ of Southern California, Dept of Electrical Engineerin,Apt. #7, Los Angeles, USA

    Yuzhuo Ren

  • University of Southern California, Los Angeles, USA

    C.-C. Jay Kuo

About the authors

Chen Chen received his B.S. degree in Electrical Engineering from Beijing University of Posts and Telecommunications (BUPT) in 2010. He received his M.S. degree in Electrical Engineering from University of Southern California (USC) in 2012. At the same year, he joined the Media Communication Lab led by Professor Kuo in University of Southern California (USC), where he is pursuing her Ph.D degree in Electrical Engineering and serving as a research assistant. His research interests include image classification, image tagging and image/video processing.

Yu-Zhuo Ren received her B.S. degree in Hebei University of Technology (HUT), China, in 2011 and the M.S. degree in Electrical Engineering from University of Southern California (USC) in 2013. She is now working as a research assistant in the Media Communication Lab led by Professor Kuo. Her research interests include image understanding related problems, in the field of computer vision and machine learning.

C.-C. Jay Kuo Dr. C.-C. Jay Kuo received the B.S. degree from the National Taiwan University, Taipei, in 1980 and the M.S. and Ph.D. degrees from the Massachusetts Institute of Technology, Cambridge, in 1985 and 1987, respectively, all in Electrical Engineering. From October 1987 to December 1988, he was Computational and Applied Mathematics Research Assistant Professor in the Department of Mathematics at the University of California, Los Angeles. Since January 1989, he has been with the University of Southern California (USC).
He is presently Director of the Multimedia Communication Lab. and Professor of Electrical Engineering and Computer Science at the USC. His research interests are in the areas of multimedia data compression, communication and networking, multimedia content analysis and modeling, and information forensics and security. Dr. Kuo has guided 119 students to their Ph.D. degrees and supervised 23 postdoctoral research fellows. Currently, his research group at the USC has around 30Ph.D. students, which is one of the largest academic research groups in multimedia technologies. He is coauthor of about 220 journal papers, 850 conference papers and 12 books. He delivered over 550 invited lectures in conferences, research institutes, universities and companies.

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