Studies in Computational Intelligence

Transcription

1 Studies in Computational Intelligence Volume 552 Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland For further volumes:

2 About this Series The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligence quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, selforganizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.

3 Yen-Wei Chen Lakhmi C. Jain Editors Subspace Methods for Pattern Recognition in Intelligent Environment ABC

4 Editors Yen-Wei Chen College of Science and Engineering Ritsumeikan University Kusuatsu Japan Lakhmi C. Jain Faculty of Education, Science, Technology and Mathematics University of Canberra ACT 2601 Australia ISSN X ISSN (electronic) ISBN ISBN (ebook) DOI / Springer Heidelberg New York Dordrecht London Library of Congress Control Number: c Springer-Verlag Berlin Heidelberg 2014 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. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. 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. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (

5 Foreword The book is devoted to the contemporary methods for pattern recognition, based on the use of the lower-dimensional feature subspace. The actuality of this scientific area is indisputable and is defined by the impetuous development of the information theories and the huge amounts of visual data, which have to be processed and analyzed. The large challenge for the pattern recognition systems is the necessity of efficient processing of multidimensional signals and images, obtained from various sources, such as (for example), the EEG and ECG devices, traffic surveillance systems, systems for video control, admission systems, remote control systems, medical systems for image diagnostics, and systems for content-based retrieval in large image databases. The book comprises 8 chapters, which present various new approaches aimed at solving some important problems in the theory of the pattern recognition, related to the feature space reduction, and also many interesting applications in the pattern recognition area. Each chapter is a small monograph, which represents the research of the authors in the corresponding area. In the first chapter are presented and investigated new approaches, based on the Active Shape Model (ASM) for face recognition. Here is also proposed the so-called Point Distribution Model, used for the description of a set of landmarks, which characterize the form of the recognized face. By using the landmarks alignment and the Principal Component Analysis (PCA), is developed the multi-resolution graylevel face profile, used for the image search. The presented modeling results prove the improvement achieved, when compared to the classical ASM. The second chapter is focused on one new approach for features space reduction through the conditional Statistical Shape Model (SSM), used in the medical image analysis. In the chapter are given the advantages and the disadvantages of this model, and are discussed its basic varieties: the non-conditional (NC-SSM), the conventional conditional (C-SSM), and the relaxed conditional Statistical Shape Model (RC-SSM). Here is also given a comparison among various SSM models, used for the segmentation of computer tomography images. The obtained experimental results for the evaluation of the shape of the segmented areas show, that the models RC-SSM and RC-SSM-E (RC-SSM with integrated conditional features error model) surpass C-SSM and NC-SSM.

6 VI Foreword In the third chapter is offered feature extraction technique based on the method for Independent Component Analysis (ICA), aimed at the classification of Highresolution Remote Sensing Multispectral (MS) Images. The transform of the color components R, G, B of the investigated 3-channel MS images into the corresponding 3 independent components IC1, IC2, and IC3 results in the obtaining of nonoverlapping distributions in the ICA features space. The evaluation of the classification results for the objects detected in the MS images through the K-means algorithm shows higher accuracy for the ICA features space, compared to that, based on the PCA. In the fourth chapter is presented the so-called Generative learning method, through which, on the basis of a small number of real images of traffic signs, obtained from TV cameras, are generated many artificial images of these signs, which have various degradations in respect to the originals. For the signs recognition is used the subspace method. The presented framework is applicable for any traffic sign by combining it with conventional traffic sign detection methods. The use of the presented results is aimed at the driver support systems. In the fifth chapter are presented two novel subspace analysis methods for face recognition and image clustering tasks. One is a nonlinear subspace method obtained by using an algebraic approach, and the other is a probabilistic subspace analysis method derived from the topic model. The experiments on face recognition and image clustering show that the proposed subspace analysis methods are resistant in respect of faces variations, such as noise, pose, and lighting. The sixth chapter is devoted to the problem for the restoration of the highresolution image from a single low-resolution input image, by using the sparse signal representation, based on the combined K-SVD algorithm and the Orthogonal Matching Pursuit (OMP) for learning the adaptive dictionary and achieving the sparse coefficients. The OMP is an extended orthogonal version of matching pursuit, which is a type of numerical technique which involves finding the best matching projections of multidimensional data onto an over-complete dictionary, and can be combined into the K-SVD strategy for achieving sparse representation and the best adaptive dictionary. The effectiveness of the proposed new dictionary propagation in sparse coding for super-resolution is demonstrated by comparison with the conventional super-resolution approaches such as sparse coding and bicubic interpolation. The seventh chapter is a short tutorial on the sampling and recovery of continu ously-defined sparse signals and its application to image feature extraction. To sample a signal at low frequency compared with its Nyquist frequency, the signal is characterized using how frequently unknown parameters appear in its parametric expression, instead of the classical frequency approach. The new frequency of the parameter appearance is defined as the rate of innovation. In the chapter are analyzed some example signals of this kind: a sequence of Diracs and piecewise polynomials, and is also given the application of the proposed algorithm for object edge detection. In the eighth chapter is developed the approach for human face representation through so-called tensorfaces. Here is proposed the tensor-based subspace learning method (TSL) for synthesizing human multi-pose facial images from a single two-dimensional (2D) image. The facial pose synthesis is applied to generate much

7 Foreword VII required information for several applications, such as public security, facial cosmetology, etc. In the proposed TSL method, 2D multi-pose images in the database are previously organized into a tensor form and a tensor decomposition technique is applied to build the projection subspaces. The experimental results show the effectiveness of proposed method for facial pose synthesis. Each chapter comprises a theoretical part, followed by experimental results and comparison with other similar techniques. The authors are researchers from different universities and R&D centers in Japan, China, and USA. The book will be very useful for university and PhD students, researchers and software developers, who work in the area of the digital processing, analysis and recognition of signals and images. Prof. D. Sc. PhD. Roumen Kountchev Technical University of Sofia Bulgaria

8 Preface With the fast development of internet and computer technologies, the amount of available data is now rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform high dimensional data to a lower dimensional space (subspace), that focuses on the relevant information only. A lot of methods have been proposed for data transformation, such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) and so on. PCA is a linear transform that projects the data into a new coordinate system (subspace) with bases where the data varies the most, while ICA finds a linear representation of non-gaussian data so that the components are statistically independent, or as independent as possible. Kernel PCA and Kernel ICA are modifications of the original PCA and ICA, facilitating nonlinear transformations. A lot of other nonlinear subspace methods are also proposed in the literature, such as ISOMAP, Locally Linear Embedding (LLE) and so on. Recently, sparse coding and sparse sampling are hot topics in pattern recognition and signal processing. For multidimensional data such as medical volumes, multi-view facial images, tensor based subspace learning methods are also proposed, which are based on a multi-linear algebra framework. This book focuses on major trends and new techniques in subspace methods and their applications in pattern recognition. There are eight chapters written by experts in this book. The area of interest of the chapters covers a broad spectrum of subspace learning methods with application to pattern recognition in intelligent environment. Chapter 1 focuses on principal component analysis (PCA) and its application to construct an active shape model of facial images. The variations of facial shape and texture can be represented by a few leading edge modes. Through this model, image interpretation can be formulated as a fitting problem. Chapter 2 introduces a conditional statistical shape model, which is a valuable subspace method in medical image analysis. During training of the model, the relationship between the shape of the object of interest and a set of conditional features is established. Subsequently, while analyzing an unseen image, a measured condition is matched with this conditional distribution and then a subspace of the

9 X Preface training data is marked as relevant and used for the desired reconstruction of the object shape. Chapter 3 focuses on independent component analysis (ICA) and its application to classification of high-resolution remote sensing images. ICA tries to find a linear representation of non-gaussian data so that the components are statistically independent, or as independent as possible. The three independent components are in opponent-color model by which the responses of R, G and B cones are combined in opponent fashions. This is consistent with the principle of many color systems. Chapter 4 presents a training method for subspace construction from artificially generated images for traffic sign recognition, which is one of the important tasks for driver support systems. Conventional approaches used camera-captured images as training data, which required exhaustive collection of captured samples. The generative learning method, instead, allows to obtain these training images based on a small set of actual images. Chapter 5 presents local structure preserving methods based on subspace analysis. Two novel subspace methods are proposed for face recognition and image clustering tasks. The first is named Supervised Kernel Locality Preserving Projections (SKLPP) for face recognition tasks, in which geometric relations are preserved according to prior class-label information and complex nonlinear variations of real face images are represented by nonlinear kernel mapping. The second is a novel probabilistic topic model for image clustering task, named Dual Local Consistency Probabilistic Latent Semantic Analysis (DLC-PLSA). The proposed DLC-PLSA model can learn an effective and robust mid-level representation in the latent semantic space for image analysis. Chapter 6 introduces the sparse signal representation, and a popular implementation: KSVD algorithm combining orthogonal matching pursuit (OMP) for learning the adaptive dictionary and achieving the sparse coefficients. The sparse representation is used for learning-based image super-resolution for recovering the high-resolution image from only single low-resolution one. Based on the couple dictionary learning for super-resolution, we proposed a HR2LR (high-resolution to low-resolution) dictionary propagation algorithm in sparse coding for image super-resolution. Chapter 7 presents a technique for efficient sampling and recovery of continuouslydefined sparse signals that is known as sparse sampling. To sample a signal at low frequency compared with its Nyquist frequency, the signal was characterized using how frequently the unknown parameters appear in its parametric expression, instead of the classical frequency. The new frequency of the parameter appearance was defined as the rate of innovation. The chapter focused on two typical examples of signals with finite rate of innovation: the sequence of Diracs and piecewise polynomials. Chapter 8 presents a tensor-based subspace learning method (TSL) for synthesizing human multi-pose facial images from a single two-dimensional image. In the proposed TSL method, two-dimensional multi-pose images in the database are previously organized into a tensor form and a tensor decomposition technique is applied to build projection subspaces. In synthesis processing, the input two-dimensional

10 Preface XI image is first projected into its corresponding projection subspace to get an identity vector and then the identity vector is used to generate other novel pose images. Although the above chapters do not make a complete coverage of the subspace learning methods for pattern recognition, it provides a flavor of the important issues and the benefits of applying subspace learning methods to pattern recognition. We are grateful to the authors and reviewers for their contribution. We would like to thank the editors of the Springer for hosting this book and for their advice during the editorial process of the book. January 2014 Yen-Wei Chen, Ritsumeikan University, Japan Lakhmi Jain, University of Canberra, Australia

11 Contents 1 Active Shape Model and Its Application to Face Alignment... 1 Huchuan Lu, Fan Yang 1 Introduction StatisticalShapeModels PointDistributionModel ModelingLocalStructure Multi-resolutionActiveShapeModel ImageSearchUsingActiveShapeModel Initial Estimate ComputetheMovementsofLandmarks ExampleofSearch ApplicationandProblems ImprovementsonClassicalActiveShapeModel Constraintonb WidthofSearchProfile Landmarks Grouping DirectionofSearchProfile Skin-ColorModel RelatedWork Conclusions References Condition Relaxation in Conditional Statistical Shape Models Elco Oost, Sho Tomoshige, Akinobu Shimizu 1 Introduction ConditionalStatisticalShapeModels TheBenefitofConditionalSSMs Reliability of the Conditional Term LevelSetBasedConditionalSSMs RelaxationoftheConditionalTerm... 39

12 XIV Contents 7 EmployingtheSelectionFormulaforRelaxation Automatic Estimation of the Reliability of the Conditional Features PerformanceComparisonofVariousConditionalSSMs Conclusions References Independent Component Analysis and Its Application to Classification of High-Resolution Remote Sensing Images Xiang-Yan Zeng, Yen-Wei Chen 1 Introduction Background of Independent Component Analysis LinearTransformationofMultivariateData BlindSourceSeparation Independent Components Analysis DataModel WhyICA? ICAAlgorithms WhiteningtheData ICAbyInformationMaximization ICA by Maximization of Non-gaussianity ICAforRemoteSensingStudy ICAforHyperspectralRemoteSensing ICAforHigh-ResolutionRemoteSensing Independent Components of RGB Remote SensingImages Classification of High-Resolution Remote Sensing Images Pixel Classification by Spectral Information Classification by Spectral Information and SpatialConsistency Conclusions References Subspace Construction from Artificially Generated Images for Traffic Sign Recognition Hiroyuki Ishida, Ichiro Ide, Hiroshi Murase 1 Introduction to the Generative Learning ModelingofDegradationCharacteristics EstimationofDegradationCharacteristics Generative Learning for Traffic Sign Recognition GenerationModelsofTrafficSigns TrainingbyGenerativeLearning ParameterEstimationStep GenerationofTrainingImages... 92

13 Contents XV 3 Recognition by the Subspace Method ConstructionofaSubspace Multiple Frame Integration CircularSignDetection Experiment Results Discussion Summary References Local Structure Preserving Based Subspace Analysis Methods and Applications Jian Cheng, Hanqing Lu 1 Introduction LocalStructurePreserving Local Structure Preserving for Face Recognition Supervised Kernel Locality Preserving Projections Experimental Results on Face Recognition LocalStructurePreservingforImageClustering plsawithlocalstructurepreserving Sparse Neighborhood Consistency LocalWordConsistency TheRegularizedModel Model Fitting ExperimentalResultsonImageClustering Conclusions References Sparse Representation for Image Super-Resolution Xian-Hua Han, Yen-Wei Chen 1 Introduction SparseCoding Orthogonal Matching Pursuit K-SVDAlgorithm SparseCodingBasedSuper-Resolution Analysis of the Represented Features for Local Image Patches HR2LRDictionaryPropagationofSC Experiments Conclusions References Sampling and Recovery of Continuously-Defined Sparse Signals and Its Applications Akira Hirabayashi 1 Introduction Signals with Finite Rate of Innovation as an Extension of Band-LimitedSignals

14 XVI Contents 3 SamplingandRecoveryoftheSequenceofDiracs NoiselessCase CadzowDenoising Maximum Likelihood Estimation Sampling and Recovery of Signals of Piecewise Polynomials ApplicationtoImageFeatureExtraction Conclusion References Tensor-Based Subspace Learning for Multi-pose Face Synthesis Xu Qiao, Takanori Igarashi, Yen-Wei Chen 1 Introduction Tensor and Multilinear Algebra Foundations DefinitionsandPreliminaries TensorDefinitions TensorNormandRank Symmetry and Diagonal Tensors MatricizationofTensors Tensor Multiplication: The n-mode Product Matrix Product Tensor Decomposition Tucker Decomposition CANDECOMP/PARAFAC Decomposition OtherDecompositions Tensor-BasedSubspaceLearningAlgorithm ImageRepresentation TensorSubspaceBuilding SynthesisProcedure ExperimentsandResults Data ImageDeformation DataCompression SynthesisResultandEvaluation Conclusion References Editors Author Index