1.

Record Nr.

UNINA9910253967503321

Autore

Chen Chen

Titolo

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

Pubbl/distr/stampa

Singapore : , : Springer Singapore : , : Imprint : Springer, , 2016

ISBN

981-10-0631-8

Edizione

[1st ed. 2016.]

Descrizione fisica

1 online resource (128 p.)

Collana

SpringerBriefs in Signal Processing, , 2196-4076

Disciplina

620

Soggetti

Signal processing

Image processing

Speech processing systems

Optical data processing

Mathematics

Visualization

Signal, Image and Speech Processing

Image Processing and Computer Vision

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

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.