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Multimedia Data Mining and Knowledge Discovery
Valery A. Petrushin and Latifur Khan (Eds)
Multimedia Data
Mining and
Knowledge Discovery
Valery A. Petrushin, MS, PhD
Accenture Technology Labs
Accenture Ltd
161 N. Clark St.
Chicago, IL 60601, USA
Latifur Khan, BS, MS, PhD
EC 31
2601 N. Floyd Rd.
Richardson, TX 75080-1407, USA
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Control Number: 2006924373
ISBN-10: 1-84628-436-8
ISBN-13: 978-1-84628-436-6
Printed on acid-free paper
© Springer-Verlag London Limited 2007
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted
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Contents
Preface....................................................................................... xvii
List of Contributors
......................................................................
xix
Part I. Introduction
1. Introduction into Multimedia Data Mining
and Knowledge Discovery
..........................................................
Valery A. Petrushin
1.1 What Is Multimedia Data Mining? ............................................
1.2 Who Does Need Multimedia Data Mining?..................................
1.3 What Shall We See in the Future? .............................................
1.4 What Can You Find in This Book? ............................................
References ...............................................................................
2. Multimedia Data Mining: An Overview
........................................
Nilesh Patel and Ishwar Sethi
2.1 Introduction ........................................................................
2.2 Multimedia Data Mining Architecture ........................................
2.3 Representative Features for Mining ...........................................
2.3.1 Feature Fusion .............................................................
2.4 Supervised Concept Mining.....................................................
2.4.1 Annotation by Classification ............................................
2.4.2 Annotation by Association ..............................................
2.4.3 Annotation by Statistical Modeling....................................
2.5 Concept Mining Through Clustering..........................................
2.6 Concept Mining Using Contextual Information.............................
2.7 Events and Feature Discovery ..................................................
2.8 Conclusion .........................................................................
References ...............................................................................
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