Big Visual Data Analysis: Scene Classification and Geometric Labeling

✍ Scribed by Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo (auth.)

• Publisher: Springer Singapore
• Year: 2016
• Tongue: English
• Leaves: 128
• Series: SpringerBriefs in Electrical and Computer Engineering
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-x
Introduction....Pages 1-5
Scene Understanding Datasets....Pages 7-21
Indoor/Outdoor Classification with Multiple Experts....Pages 23-63
Outdoor Scene Classification Using Labeled Segments....Pages 65-92
Global-Attributes Assisted Outdoor Scene Geometric Labeling....Pages 93-120
Conclusion and Future Work....Pages 121-122

✦ Subjects

Signal, Image and Speech Processing; Image Processing and Computer Vision; Visualization