Knowledge Computing and its Applications

Probabilistic graphical models (PGM) are one of the rich frameworks. These models are used over complex domains for coding probability distributions. The joint distributions interact with each other over large numbers of random variables and are the combination of statistics and computer science. These concepts are dependent on theories such as probability theory, graph algorithms, machine learning, which make a basic tool in devising many machine learning problems. These are the origin for the contemporary methods in an extensive range of applications. These applications range as medical diagnosis, image understanding, speech recognition, natural language processing, etc. Graphical models are one of dominant tools for handling image processing applications. On the other hand, the volume of image data gives rise to a problem. The representation of all possible graphical model node variables with that of discrete states heads to the number of states for the model. This leads to interpretation computationally obstinate. Many projects involve a human intervention or an automated system to obtain the consensus established on existing information. The PGM, discussed in this chapter, offers a variety of approaches. The approach is based on models and allows interpretable models to be built which then is employed by reasoning algorithms. These models are also studied significantly from data and allow the approaches for cases where the model is manually built. Most real-world applications are of uncertain data which makes a model building more challenging. This chapter emphasizes on PGM where the uncertainty of data is obvious. PGM provides models that are more realistic. These are extended from Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data also. For each class of models, the S. Tumula (&) Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology (A), CBIT post, Gandipet, Hyderabad, Telangana, India e-mail: sritumula@gmail.com S. S. Fathima Department of Computer Science and Engineering, Osmania University, Hyderabad, Telangana, India © Springer Nature Singapore Pte Ltd. 2018 S. Margret Anouncia and U. K. Wiil (eds.), Knowledge Computing and its Applications, https://doi.org/10.1007/978-981-10-8258-0_1 3 chapter describes the fundamental bases: representation, inference, and learning. Finally, the chapter considers the decision making under the uncertainty of the data.

[1]  Baochang Zhang,et al.  Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor , 2010, IEEE Transactions on Image Processing.

[2]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[4]  Marc Acheroy,et al.  Automatic 3D face authentication , 2000, Image Vis. Comput..

[5]  Di Huang,et al.  Local Binary Patterns and Its Application to Facial Image Analysis: A Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Oksam Chae,et al.  Local directional pattern variance (ldpv): a robust feature descriptor for facial expression recognition , 2012, Int. Arab J. Inf. Technol..

[7]  Jiashu Zhang,et al.  Face recognition with enhanced local directional patterns , 2013, Neurocomputing.

[8]  Pang Yan-wei.  A Novel Gabor-LDA Based Face Recognition Method , 2006 .

[9]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[10]  Stefano Battiston,et al.  A model of a trust-based recommendation system on a social network , 2006, Autonomous Agents and Multi-Agent Systems.

[11]  Xiaojun Jing,et al.  Local Gabor Dominant Direction Pattern for Face Recognition , 2015 .

[12]  Okko Johannes Räsänen,et al.  Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits , 2015, Comput. Speech Lang..

[13]  Antonio Moreno,et al.  SigTur/E-Destination: Ontology-based personalized recommendation of Tourism and Leisure Activities , 2013, Eng. Appl. Artif. Intell..

[14]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[15]  Vijay V. Raghavan,et al.  A critical investigation of recall and precision as measures of retrieval system performance , 1989, TOIS.

[16]  S. Saraswathi,et al.  Concept Based Dynamic Ontology Creation for Job Recommendation System , 2016 .

[17]  R. Choras.  Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems , 2008 .

[18]  Mohammed Benjelloun,et al.  Spine Localization in X-ray Images Using Interest Point Detection , 2009, Journal of Digital Imaging.

[19]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[20]  Garrison W. Cottrell,et al.  Non-Linear Dimensionality Reduction , 1992, NIPS.

[21]  Xiaoou Tang,et al.  Recent Advances in Subspace Analysis for Face Recognition , 2004, SINOBIOMETRICS.

[22]  Haesun Park,et al.  Generalizing discriminant analysis using the generalized singular value decomposition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Rung Ching Chen,et al.  A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection , 2012, Expert Syst. Appl..

[24]  P. V. S. S. R. Chandra Mouli,et al.  Two-level dimensionality reduced local directional pattern for face recognition , 2016, Int. J. Biom..

[25]  Wen Gao,et al.  Component-Based Cascade Linear Discriminant Analysis for Face Recognition , 2004, SINOBIOMETRICS.

[26]  Joongmin Choi,et al.  An Ontology-Based Recommendation System Using Long-Term and Short-Term Preferences , 2011, 2011 International Conference on Information Science and Applications.

[27]  Ferdinando Silvestro Samaria,et al.  Face recognition using Hidden Markov Models , 1995 .

[28]  Gábor Horváth,et al.  Segmentation of Anatomical Structures on Chest Radiographs , 2010 .

[29]  Narendra Ahuja,et al.  Learning recognition and segmentation of 3-D objects from 2-D images , 1993, 1993 (4th) International Conference on Computer Vision.

[30]  Markus Zanker,et al.  Preference reasoning with soft constraints in constraint-based recommender systems , 2010, Constraints.

[31]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[32]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[33]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[34]  Christoph von der Malsburg,et al.  Recognizing Faces by Dynamic Link Matching , 1996, NeuroImage.

[35]  Hujun Yin,et al.  Face Recognition Using RBF Neural Networks and Wavelet Transform , 2005, ISNN.

[36]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[37]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

[38]  A. Gulati,et al.  A ‘two-in-one’ foreign body coin in oesophagus: A case report , 2014 .

[39]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[40]  Shirley Gregor,et al.  Explanations from knowledge-based systems and cooperative problem solving: an empirical study , 2001, Int. J. Hum. Comput. Stud..

[41]  Cristian Lorenz,et al.  Automated model-based vertebra detection, identification, and segmentation in CT images , 2009, Medical Image Anal..

[42]  B. Saravana Balaji,et al.  Fuzzy service conceptual ontology system for cloud service recommendation , 2016, Comput. Electr. Eng..

[43]  Gerald Reif,et al.  SemTree: Ontology-Based Decision Tree Algorithm for Recommender Systems , 2008, SEMWEB.

[44]  L. Upreti,et al.  Imaging for diagnosis of foreign body aspiration in children? , 2015, Indian pediatrics.

[45]  Takayuki Ito,et al.  An implementation of a knowledge recommendation system based on similarity among users' profiles , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[46]  Xiaojun Qi,et al.  Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns , 2015, IET Comput. Vis..

[47]  Oksam Chae,et al.  Local Directional Number Pattern for Face Analysis: Face and Expression Recognition , 2013, IEEE Transactions on Image Processing.

[48]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[49]  Wen Gao,et al.  The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[50]  Guy Shani,et al.  A Survey of Accuracy Evaluation Metrics of Recommendation Tasks , 2009, J. Mach. Learn. Res..

[51]  Rafael C. González,et al.  Digital image processing, 3rd Edition , 2008 .

[52]  Masoud Gholamian,et al.  Detection of Foreign Bodies by Spiral Computed Tomography and Cone Beam Computed Tomography in Maxillofacial Regions , 2014, Journal of dental research, dental clinics, dental prospects.

[53]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[54]  Hanqing Lu,et al.  Face detection using improved LBP under Bayesian framework , 2004, Third International Conference on Image and Graphics (ICIG'04).

[55]  Maurizio Morisio,et al.  An Ontology-based contextual pre-filtering technique for Recommender Systems , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[56]  Gwen Littlewort,et al.  Recognizing facial expression: machine learning and application to spontaneous behavior , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[57]  Anil K. Jain,et al.  Component-Based Representation in Automated Face Recognition , 2013, IEEE Transactions on Information Forensics and Security.

[58]  Oksam Chae,et al.  Local Gabor directional pattern for facial expression recognition , 2012, 2012 15th International Conference on Computer and Information Technology (ICCIT).

[59]  Faisal Ahmed,et al.  Gradient directional pattern: A robust feature descriptor for facial expression recognition , 2012 .

[60]  Hanqing Lu,et al.  Improving kernel Fisher discriminant analysis for face recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[61]  Thomas Serre,et al.  A Component-based Framework for Face Detection and Identification , 2007, International Journal of Computer Vision.

[62]  Alaa Eleyan,et al.  Face Recognition System Based on PCA and Feedforward Neural Networks , 2005, IWANN.

[63]  Xiaojun Qi,et al.  Face recognition under varying illuminations using logarithmic fractal dimension-based complete eight local directional patterns , 2016, Neurocomputing.

[64]  Xiaojun Qi,et al.  Face recognition under illumination variations based on eight local directional patterns , 2015, IET Biom..

[65]  Michael Scholz,et al.  Measuring consumers' willingness to pay with utility-based recommendation systems , 2015, Decis. Support Syst..

[66]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[67]  Guy Shani,et al.  Evaluating Recommender Systems , 2015, Recommender Systems Handbook.

[68]  L. Ladha,et al.  FEATURE SELECTION METHODS AND ALGORITHMS , 2011 .

[69]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[70]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[71]  Wilfried N. Gansterer,et al.  On the Relationship Between Feature Selection and Classification Accuracy , 2008, FSDM.

[72]  Jay F. Nunamaker,et al.  User Acceptance of Knowledge-Based System Recommendations: Explanations, Arguments, and Fit , 2015, Decis. Support Syst..

[73]  Tieniu Tan,et al.  Combining Statistics of Geometrical and Correlative Features for 3D Face Recognition , 2006, BMVC.

[74]  P. V. S. S. R. Chandra Mouli,et al.  Dimensionality reduced local directional pattern (DR-LDP) for face recognition , 2016, Expert Syst. Appl..

[75]  Satish Kumar Singh,et al.  Local directional gradient pattern: a local descriptor for face recognition , 2022, Multimedia Tools and Applications.

[76]  Xin Yang,et al.  Face Recognition Using Direct-Weighted LDA , 2004, PRICAI.

[77]  Oksam Chae,et al.  Local Gaussian Directional Pattern for face recognition , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[78]  Wang Yin Chai,et al.  X-Ray Image Enhancement Using a Boundary Division Wiener Filter and Wavelet-Based Image Fusion Approach , 2016, J. Inf. Process. Syst..

[79]  Shiwei Tang,et al.  Face recognition using improved pairwise coupling support vector machines , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[80]  Alexander Felfernig,et al.  Constraint-based recommender systems: technologies and research issues , 2008, ICEC.

[81]  Hong Yang,et al.  A LBP-based Face Recognition Method with Hamming Distance Constraint , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[82]  Raihan Firoz,et al.  Medical Image Enhancement Using Morphological Transformation , 2016 .

[83]  Yiding Wang,et al.  A Robust Method for Near Infrared Face Recognition Based on Extended Local Binary Pattern , 2007, ISVC.

[84]  Mythili Thirugnanam,et al.  A framework for automatic intrude object identification in paediatric foreign body aspired radiography images , 2016 .

[85]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[86]  Z. Hassan,et al.  Double coin in esophagus at same location and same alignment - a rare occurrence: a case report , 2009, Cases journal.

[87]  Shengcai Liao,et al.  Learning Multi-scale Block Local Binary Patterns for Face Recognition , 2007, ICB.

[88]  Takeo Kanade,et al.  Picture Processing System by Computer Complex and Recognition of Human Faces , 1974 .

[89]  David Zhang,et al.  A Fourier-LDA approach for image recognition , 2005, Pattern Recognit..

[90]  Oksam Chae,et al.  Facial expression recognition based on Local Sign Directional Pattern , 2012, 2012 19th IEEE International Conference on Image Processing.

[91]  D. Fishman,et al.  Management of ingested foreign bodies in children: a clinical report of the NASPGHAN Endoscopy Committee. , 2015, Journal of pediatric gastroenterology and nutrition.

[92]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[93]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[94]  Oksam Chae,et al.  Local Directional Pattern (LDP) for face recognition , 2010, 2010 Digest of Technical Papers International Conference on Consumer Electronics (ICCE).

[95]  Mohammed Benjelloun,et al.  Fully automatic vertebra detection in x-ray images based on multi-class SVM , 2012, Medical Imaging.