Big Visual Data Analysis Scene Classification and Geometric Labeling [Paperback]

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. It is illustrated with numerous naturaland synthetic color images,and extensive statistical analysis is provided to help readers visualize big visualdata distribution and the associatedproblems. Although therehas been some research on big visual data analysis, little workhas been published on big image data distribution analysis using the modernstatistical approach described in thisbook. By presenting a complete methodology on big visual data analysis withthree illustrative scene comprehensionproblems, it provides ageneric framework that canbe applied to other big visual data analysis tasks.Introduction.-Scene Understanding Datasets.- Indoor/Outdoor classi
cation with MultipleExperts.- Outdoor Scene Classi
cation Using Labeled Segments.- Global-AttributesAssisted Outdoor Scene Geometric Labeling.- Conclusion and Future Work.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 unlc+