Big Visual Data Analysis Scene Classification and Geometric Labeling
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 estimat
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Chen Chen Yuzhuo Ren C.-C. Jay Kuo
Big Visual Data Analysis Scene Classification and Geometric Labeling 123
SpringerBriefs in Electrical and Computer Engineering Signal Processing
Series editors Woon-Seng Gan, Singapore, Singapore C.-C. Jay Kuo, Los Angeles, USA Thomas Fang Zheng, Beijing, China Mauro Barni, Siena, Italy
More information about this series at http://www.springer.com/series/11560
Chen Chen Yuzhuo Ren C.-C. Jay Kuo •
•
Big Visual Data Analysis Scene Classification and Geometric Labeling
123
Chen Chen Department of Electrical Engineering University of Southern California Los Angeles, CA USA
C.-C. Jay Kuo University of Southern California Los Angeles, CA USA
Yuzhuo Ren Department of Electrical Engineering University of Southern California Los Angeles, CA USA
ISSN 2191-8112 ISSN 2191-8120 (electronic) SpringerBriefs in Electrical and Computer Engineering ISSN 2196-4076 ISSN 2196-4084 (electronic) SpringerBriefs in Signal Processing ISBN 978-981-10-0629-6 ISBN 978-981-10-0631-9 (eBook) DOI 10.1007/978-981-10-0631-9 Library of Congress Control Number: 2016932343 © The Author(s) 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Science+Business Media Singapore Pte Ltd.
Dedicated to my wife and my parents, for their love and endless support —Chen Chen Dedicated to my parents for their endless love and encouragement —Yuzhuo Ren Dedicated to my wife for her long-term understanding and support —C.-C. Jay Kuo
Preface
Scene understanding is a key issue in computer vision, which recognizes scene image semantic contents and their corresponding contexts. As one of the most challenging scene understanding problems, scene classification considers the semantic concepts in a scene image and classifies scene images into their associated scene categories. Meanwhile, geometric labeling focuses on the scene layouts, where image pixels are labeled and gro
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