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Multimedia Data Mining and Analytics

Overview of attention for book
Cover of 'Multimedia Data Mining and Analytics'

Table of Contents

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    Book Overview
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    Chapter 1 Disruptive Innovation: Large Scale Multimedia Data Mining
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    Chapter 2 Sentiment Analysis Using Social Multimedia
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    Chapter 3 Twitter as a Personalizable Information Service
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    Chapter 4 Mining Popular Routes from Social Media
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    Chapter 5 Social Interactions over Location-Aware Multimedia Systems
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    Chapter 6 In-house Multimedia Data Mining
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    Chapter 7 Content-Based Privacy for Consumer-Produced Multimedia
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    Chapter 8 Large-Scale Biometric Multimedia Processing
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    Chapter 9 Detection of Demographics and Identity in Spontaneous Speech and Writing
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    Chapter 10 Evaluating Web Image Context Extraction
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    Chapter 11 Content Based Image Search for Clothing Recommendations in E-Commerce
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    Chapter 12 Video Retrieval Based on Uncertain Concept Detection Using Dempster–Shafer Theory
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    Chapter 13 Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video
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    Chapter 14 Mining Videos for Features that Drive Attention
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    Chapter 15 Exposing Image Tampering with the Same Quantization Matrix
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    Chapter 16 Fast Binary Embedding for High-Dimensional Data
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    Chapter 17 Fast Approximate $$K$$ K -Means via Cluster Closures
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    Chapter 18 Fast Neighborhood Graph Search Using Cartesian Concatenation
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    Chapter 19 Listen to the Sound of Data
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3 X users
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Title
Multimedia Data Mining and Analytics
Published by
Springer International Publishing, March 2015
DOI 10.1007/978-3-319-14998-1
ISBNs
978-3-31-914997-4, 978-3-31-914998-1
Editors

Baughman, Aaron K., Gao, Jiang, Pan, Jia-Yu, Petrushin, Valery A.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
Unknown 31 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 28%
Student > Master 5 16%
Student > Bachelor 4 13%
Student > Doctoral Student 3 9%
Researcher 3 9%
Other 6 19%
Unknown 2 6%
Readers by discipline Count As %
Computer Science 12 38%
Engineering 6 19%
Business, Management and Accounting 5 16%
Economics, Econometrics and Finance 2 6%
Environmental Science 1 3%
Other 5 16%
Unknown 1 3%