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Big Visual Data Analysis : Scene Classification and Geometric Labeling / / by Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo



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Autore: Chen Chen Visualizza persona
Titolo: Big Visual Data Analysis : Scene Classification and Geometric Labeling / / by Chen Chen, Yuzhuo Ren, C.-C. Jay Kuo Visualizza cluster
Pubblicazione: Singapore : , : Springer Singapore : , : Imprint : Springer, , 2016
Edizione: 1st ed. 2016.
Descrizione fisica: 1 online resource (128 p.)
Disciplina: 620
Soggetto topico: Signal processing
Image processing
Speech processing systems
Optical data processing
Mathematics
Visualization
Signal, Image and Speech Processing
Image Processing and Computer Vision
Persona (resp. second.): RenYuzhuo
KuoC.-C. Jay
Note generali: Description based upon print version of record.
Nota di bibliografia: Includes bibliographical references.
Nota di contenuto: 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.
Sommario/riassunto: 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.
Titolo autorizzato: Big Visual Data Analysis  Visualizza cluster
ISBN: 981-10-0631-8
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910253967503321
Lo trovi qui: Univ. Federico II
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Serie: SpringerBriefs in Signal Processing, . 2196-4076