Big Visual Data Analysis - Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo

Big Visual Data Analysis

Scene Classification and Geometric Labeling
Buch | Softcover
122 Seiten
2016 | 1st ed. 2016
Springer Verlag, Singapore
978-981-10-0629-6 (ISBN)
53,49 inkl. MwSt
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, outdoorscene classification, and outdoor scene layout estimation.
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.

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.

Introduction.-
Scene Understanding Datasets.- Indoor/Outdoor classification with Multiple
Experts.- Outdoor Scene Classification Using Labeled Segments.- Global-Attributes
Assisted Outdoor Scene Geometric Labeling.- Conclusion and Future Work.

Erscheinungsdatum
Reihe/Serie SpringerBriefs in Electrical and Computer Engineering
SpringerBriefs in Signal Processing
Zusatzinfo 12 Illustrations, color; 82 Illustrations, black and white; X, 122 p. 94 illus., 12 illus. in color.
Verlagsort Singapore
Sprache englisch
Maße 155 x 235 mm
Themenwelt Informatik Grafik / Design Digitale Bildverarbeitung
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Mathematik / Informatik Mathematik Graphentheorie
Technik Elektrotechnik / Energietechnik
Schlagworte Big Visual Data Analysis • Indoor/Outdoor Classification • Outdoor Scene Classification • Outdoor Scene Geometric Labeling • Scene Understanding
ISBN-10 981-10-0629-6 / 9811006296
ISBN-13 978-981-10-0629-6 / 9789811006296
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