Artificial Neural Networks in Pattern Recognition: 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11-13, 2010. Proceedings

283 Pages·2010·1.61 MB·English

by  Ahmed Al-Ani

Lecture Notes in Artificial Intelligence 5998
EditedbyR.Goebel,J.Siekmann,andW.Wahlster
Subseries of Lecture Notes in Computer Science
Friedhelm Schwenker Neamat El Gayar (Eds.)
Artificial
Neural Networks
in Pattern Recognition
4th IAPR TC3 Workshop, ANNPR 2010
Cairo, Egypt, April 11-13, 2010
Proceedings
1 3
SeriesEditors
RandyGoebel,UniversityofAlberta,Edmonton,Canada
JörgSiekmann,UniversityofSaarland,Saarbrücken,Germany
WolfgangWahlster,DFKIandUniversityofSaarland,Saarbrücken,Germany
VolumeEditors
FriedhelmSchwenker
UniversityofUlm,DepartmentofNeuralInformationProcessing
ObererEselsberg,89069,Ulm,Germany
E-mail:[email protected]
NeamatElGayar
NileUniversity,DepartmentofInformationTechnology
SmartVillage-B115,AlexDessertRoad,12677Giza(Cairo),Egypt
E-mail:[email protected]
LibraryofCongressControlNumber:2010922360
CRSubjectClassification(1998):I.2,I.4,H.3,I.5,J.3,F.1
LNCSSublibrary:SL7–ArtificialIntelligence
ISSN 0302-9743
ISBN-10 3-642-12158-6SpringerBerlinHeidelbergNewYork
ISBN-13 978-3-642-12158-6SpringerBerlinHeidelbergNewYork
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Preface
The 4th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recog-
nition, ANNPR 2010, was held at Nile University (Egypt), April 11–13, 2010.
The workshop was organized by the Technical Committee on Neural Networks
and Computational Intelligence (TC3) that is one of the 20 technical commit-
tees(TC)ofthe InternationalAssociationforPatternRecognition(IAPR).The
scope of TC3 includes computationalintelligence approaches,such as fuzzy sys-
tems, evolutionary computing and artificial neural networks and their use in
various pattern recognition applications.
The major topics of ANNPR are supervised and unsupervised learning, fea-
ture selection, pattern recognition in signal and image processing, and applica-
tions in data mining or bioinformatics. High quality across such a diverse field
of research is achieved through a rigorous and selective review process. For this
workshop, 42 papers were submitted and 23 of them were selected for inclu-
sioninthe proceedings.The workshopwasenrichedby three invited talks given
by Barbara Hammer, University of Bielefeld, Germany, Amir F. Atiya, Cairo
University, Egypt, and Mohamed Kamel, University of Waterloo, Canada.
We would like to thank all authors for the effort they put into their submis-
sions, and the Scientific Committee for taking the time to provide high-quality
reviews and selecting the best contributions for the final workshop program.
Special thanks are due to the members of the Nile University Organizing Com-
mittee,AhmedSalah,AmiraElBaroudy,EsraaAly,HebaEzzat,NesrineSameh,
Rana Salah and Mohamed Zahhar for their indispensable contributions to the
registration management and local organization.
ANNPR 2010 was supported by the International Association for Pattern
Recognition, the Center for Informatic Sciences at the Nile University, Egypt,
andtheInstituteofNeuralInformationProcessingattheUniversityofUlm.We
wish to express our appreciationto our two financialsponsors:The Information
Technology Industry Development Agency (ITIDA), which is the main sponsor
of the event, and the Microsoft Innovation Laboratory in Cairo (CMIC). We
are grateful to Springer for publishing the ANNPR 2010 proceedings in their
LNCS/LNAI series.
January 2010 Neamat El Gayar
Friedhelm Schwenker
Organization
Organizing Committee
Friedhelm Schwenker University of Ulm, Germany
Neamat El Gayar Nile University, Cairo, Egypt
Program Committee
Shigeo Abe Jos´e Manuel Inesta Lionel Prevost
Amir Atiya Rudolf Kruse Fabio Roli
Monica Bianchini Miroslav Kubat Edmondo Trentin
Horst Bunke Cheng-Lin Liu Michel Verleysen
Hisham El Shishiny Marco Maggini Terry Windeatt
Markus Hagenbuchner Simone Marinai
Barbara Hammer Erkki Oja
Tom Heskes Gu¨nther Palm
Sponsoring Institutions
Nile University, Egypt
The Information Technology Industry Development Agency (ITIDA)
Microsoft Innovation Laboratory in Cairo (CMIC)
University of Ulm, Germany
International Association for Pattern Recognition (IAPR)
Table of Contents
Supervised Learning
Pattern Classification Using a Penalized Likelihood Method ........... 1
Ahmed Al-Ani and Amir F. Atiya
Evaluation of Feature Selection by Multiclass Kernel Discriminant
Analysis ........................................................ 13
Tsuneyoshi Ishii and Shigeo Abe
Correlation-Basedand Causal Feature Selection Analysis for Ensemble
Classifiers....................................................... 25
Rakkrit Duangsoithong and Terry Windeatt
A New Monte Carlo-BasedError Rate Estimator .................... 37
Ahmed Hefny and Amir F. Atiya
Recognition of Sequences of Graphical Patterns...................... 48
Edmondo Trentin, ShuJia Zhang, and Markus Hagenbuchner
Maximum Echo-State-LikelihoodNetworks for Emotion Recognition.... 60
Edmondo Trentin, Stefan Scherer, and Friedhelm Schwenker
Robustness Analysis of Eleven Linear Classifiers in Extremely
High–Dimensional Feature Spaces.................................. 72
Ludwig Lausser and Hans A. Kestler
Unsupervised Learning
GlobalCoordinationBasedonMatrix NeuralGas for Dynamic Texture
Synthesis ....................................................... 84
Banchar Arnonkijpanich and Barbara Hammer
SIC-Means: A Semi-fuzzy Approachfor Clustering Data Streams Using
C-Means........................................................ 96
Amr Magdy and Mahmoud K. Bassiouny
The Mathematics of Divergence Based Online Learning in Vector
Quantization .................................................... 108
Thomas Villmann, Sven Haase, Frank-Michael Schleif,
Barbara Hammer, and Michael Biehl
Cluster Analysis of Cortical Pyramidal Neurons Using SOM........... 120
Andreas Schierwagen, Thomas Villmann, Alan Alp´ar, and
Ulrich Ga¨rtner
VIII Table of Contents
ParallelizedKernel Patch Clustering................................ 131
Stefan Faußer and Friedhelm Schwenker
Visual Pattern Recognition
Neural Network Cascade for Facial Feature Localization .............. 141
Thibaud Senechal, Lionel Prevost, and Shehzad Muhammad Hanif
A Hidden Markov Model Based Approach for Facial Expression
Recognition in Image Sequences ................................... 149
Miriam Schmidt, Martin Schels, and Friedhelm Schwenker
Analysis, Interpretation, and Recognition of Facial Action Units and
Expressions Using Neuro-Fuzzy Modeling ........................... 161
Mahmoud Khademi, Mohammad Hadi Kiapour,
Mohammad T. Manzuri-Shalmani, and Ali A. Kiaei
Content-Based Retrieval and Classification of Ultrasound Medical
Images of Ovarian Cysts .......................................... 173
Abu Sayeed Md. Sohail, Prabir Bhattacharya, Sudhir P. Mudur,
Srinivasan Krishnamurthy, and Lucy Gilbert
Applications
ANovelWordSpottingAlgorithmUsingBidirectionalLongShort-Term
Memory Neural Networks ......................................... 185
Volkmar Frinken, Andreas Fischer, and Horst Bunke
SwarmBasedFuzzy DiscriminantAnalysis for Multifunction Prosthesis
Control......................................................... 197
Rami N. Khushaba, Ahmed Al-Ani, and Adel Al-Jumaily
Bayesian Learning of Generalized Gaussian Mixture Models on
Biomedical Images ............................................... 207
Tarek Elguebaly and Nizar Bouguila
Defective Areas Identification in Aircraft Components by Bivariate
EMD Analysis of Ultrasound Signals ............................... 219
Marco Leo, David Looney, Tiziana D’Orazio, and Danilo P. Mandic
Different Regions Identification in Composite Strain-Encoded
(C-SENC) Images Using Machine Learning Techniques ............... 231
Abdallah G. Motaal, Neamat El-Gayar, and Nael F. Osman
Exploiting Neural Networks to Enhance Trend Forecasting for Hotels
Reservations .................................................... 241
Athanasius Zakhary, Neamat El Gayar, and Sanaa El-Ola. H. Ahmed
Table of Contents IX
VLSI Architecture of the Fuzzy Fingerprint Vault System............. 252
Sung Jin Lim, Seung-Hoon Chae, and Sung Bum Pan
Invited Talk
Clustering Very Large Dissimilarity Data Sets ....................... 259
Barbara Hammer and Alexander Hasenfuss
Author Index.................................................. 275
Pattern Classification Using a Penalized
Likelihood Method
Ahmed Al-Ani1 and Amir F. Atiya2
1 Faculty of Engineering and Information Technology, Univesity of Technology,
Sydney,Australia
[email protected]
2 Department of Computer Engineering, Cairo University,Giza, Egypt
[email protected]
Abstract. Penalized likelihood is a well-known theoretically justified
approach that has recently attracted attention by the machine learning
society. The objective function of the Penalized likelihood consists of
the log likelihood of the data minus some term penalizing non-smooth
solutions. Subsequently, maximizing this objective function would lead
tosomesortoftrade-off betweenthefaithfulnessandthesmoothness of
thefit.Therehasbeenalotofresearch toutilizepenalized likelihood in
regression,however,itisstilltobethoroughlyinvestigatedinthepattern
classificationdomain.WeproposetouseapenaltytermbasedontheK-
nearest neighbors and an iterative approach to estimate the posterior
probabilities.Inaddition,insteadoffixingthevalueofK forallpattern,
wedevelopedavariableK approach,wherethenumberofneighborscan
vary from one sample to another. The chosen value of K for a given
testing sample is influenced by the K values of its surrounding training
samples as well as the most successful K value of all training samples.
Comparison with anumberof well-known classification methodsproved
thepotential of theproposed method.
1 Introduction
The basic concept behind penalized likelihood is that a good model should pos-
sesses two indispensable properties: the goodness of fit and the smoothness of
the fit [1], [2]. However, as these two are primarily conflicting goals, a trade-off
that suits the given application is pursued. The penalized likelihood approach
seekstoachievethattrade-offbydefininganoverallobjectivefunctionconsisting
of the log-likelihood of the data minus a roughness measure, and subsequently
maximizing this objective function. The likelihood function is a measure of the
faithfulnessofthefit,whilethe roughnessfunctionisapenaltytermthatpenal-
izes non-smooth solutions.
Anexampleoftheroughnessfunctionistheintegralofthesquareofthesecond
derivativeofthefunction,leadingtothefollowingobjectivefunction(see[3]):
(cid:2)
T =log likelihood −λ f(cid:2)(cid:2)2(x)dx (1)
F.SchwenkerandN.ElGayar(Eds.):ANNPR2010,LNAI5998,pp.1–12,2010.
(cid:3)c Springer-VerlagBerlinHeidelberg2010