Big Visual Data Analysis Scene Classification and Geometric Labeling /

• Κύριοι συγγραφείς: Chen, Chen (Συγγραφέας), Ren, Yuzhuo (Συγγραφέας), Kuo, C.-C. Jay (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2016.
• Έκδοση: 1st ed. 2016.
• Σειρά: SpringerBriefs in Electrical and Computer Engineering,
• Θέματα: Engineering.; Image processing.; Mathematics.; Visualization.; Signal, Image and Speech Processing.; Image Processing and Computer Vision.

 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. 

 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.