Machine Learning Techniques for Multimedia Analysis

Slim Essid; Marine Campedel; Gaël Richard; Tomas Piatrik; Rachid Benmokhtar; Benoit Huet

doi:10.1002/9781119970231.ch5

Machine Learning Techniques for Multimedia Analysis

Slim Essid
, Marine Campedel
, Gaël Richard
, Tomas Piatrik
, Rachid Benmokhtar
, Benoit Huet

Telecom Paris
Queen Mary University of London
Eurecom

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Original language	English
Title of host publication	Multimedia Semantics
Subtitle of host publication	Metadata, Analysis and Interaction
Publisher	John Wiley and Sons
Pages	59-80
Number of pages	22
ISBN (Print)	9780470747001
DOIs	https://doi.org/10.1002/9781119970231.ch5
Publication status	Published - 8 Aug 2011

Keywords

Artificial neural networks (ANNs), or neural networks - information processing systems, generalizing mathematical models of human cognition and neural biology
Classification, best-known supervised and unsupervised approaches - multimedia data management context
Feature selection, alternative - to feature transform techniques, as PCA
Gaussian mixture model (GMM) - used in speech community
Kernel trick, success of FDA and SVM - linearity assumption avoided
Machine learning techniques - for multimedia analysis
Machine learning, in learning models - for very well-known objects or settings
Main approaches, for setting up automatic multimedia analysis system
Photograph classification problem - the class, defined by location
Schematic architecture - for automatic classification system

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10.1002/9781119970231.ch5

Cite this

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title = "Machine Learning Techniques for Multimedia Analysis",

keywords = "Artificial neural networks (ANNs), or neural networks - information processing systems, generalizing mathematical models of human cognition and neural biology, Classification, best-known supervised and unsupervised approaches - multimedia data management context, Feature selection, alternative - to feature transform techniques, as PCA, Gaussian mixture model (GMM) - used in speech community, Kernel trick, success of FDA and SVM - linearity assumption avoided, Machine learning techniques - for multimedia analysis, Machine learning, in learning models - for very well-known objects or settings, Main approaches, for setting up automatic multimedia analysis system, Photograph classification problem - the class, defined by location, Schematic architecture - for automatic classification system",

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T1 - Machine Learning Techniques for Multimedia Analysis

AU - Essid, Slim

AU - Campedel, Marine

AU - Richard, Gaël

AU - Piatrik, Tomas

AU - Benmokhtar, Rachid

AU - Huet, Benoit

PY - 2011/8/8

Y1 - 2011/8/8

KW - Artificial neural networks (ANNs), or neural networks - information processing systems, generalizing mathematical models of human cognition and neural biology

KW - Classification, best-known supervised and unsupervised approaches - multimedia data management context

KW - Feature selection, alternative - to feature transform techniques, as PCA

KW - Gaussian mixture model (GMM) - used in speech community

KW - Kernel trick, success of FDA and SVM - linearity assumption avoided

KW - Machine learning techniques - for multimedia analysis

KW - Machine learning, in learning models - for very well-known objects or settings

KW - Main approaches, for setting up automatic multimedia analysis system

KW - Photograph classification problem - the class, defined by location

KW - Schematic architecture - for automatic classification system

UR - https://www.scopus.com/pages/publications/84886142115

U2 - 10.1002/9781119970231.ch5

DO - 10.1002/9781119970231.ch5

M3 - Chapter

AN - SCOPUS:84886142115

SN - 9780470747001

SP - 59

EP - 80

BT - Multimedia Semantics

PB - John Wiley and Sons

ER -

Machine Learning Techniques for Multimedia Analysis

Keywords

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