Feature Selection and Recognition of Muzzle Point Image Pattern of Cattle by Using Hybrid Chaos BFO and PSO Algorithms

Recognition of cattle based on muzzle point image pattern (nose print) is a well study problem in the field of animal biometrics, computer vision, pattern recognition and various application domains. Missed cattle, false insurance claims and relocation at slaughter houses are major problems throughout the world. Muzzle pattern of cattle is a suitable biometric trait to recognize them by extracted features from muzzle pattern by using computer vision and pattern recognition approaches. It is similar to human’s fingerprint recognition. However, the accuracy of animal biometric recognition systems is affected due to problems of low illumination condition, pose and recognition of animal at given distance. Feature selection is known to be a critical step in the design of pattern recognition and classifier for several reasons. It selects a discriminant feature vector set or pre-specified number of features from muzzle pattern database that leads to the best possible performance of the entire classifier in muzzle recognition of cattle. This book chapter presents a novel method of feature selection by using Hybrid Chaos Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO) techniques. It has two parts: first, two types of chaotic mappings are introduced in different phase of hybrid algorithms which preserve the diversity of population and improve the global searching capability; (2) this book chapter exploited holistic feature approaches: Principal Component Analysis (PCA), Local Discriminant Analysis (LDA) and Discrete Cosine Transform (DCT) [28, 85] extract feature from the muzzle pattern images of cattle. Then, feature (eigenvector), fisher face and DCT feature vector are selected by applying hybrid PSO and BFO metaheuristic approach; it quickly find out the subspace of feature that is most beneficial to classification and recognition of muzzle pattern of cattle. This chapter provides with the stepping stone for future researches to unveil how swarm intelligence algorithms can solve the complex optimization problems and feature selection with helps to improve the cattle identification accuracy.

[1]  Navdeep Kaur,et al.  Face Recognition Using Bacteria Foraging Optimization-Based Selected Features , 2011 .

[2]  Quanmin Zhu,et al.  Complex System Modelling and Control Through Intelligent Soft Computations , 2016, Studies in Fuzziness and Soft Computing.

[3]  Ningning Yang,et al.  Particle swarm optimization for SNP haplotype reconstruction problem , 2008, Appl. Math. Comput..

[4]  Abdolreza Hatamlou,et al.  Black hole: A new heuristic optimization approach for data clustering , 2013, Inf. Sci..

[5]  Chukwudi Anyakoha,et al.  A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications , 2008, Natural Computing.

[6]  Sundarapandian Vaidyanathan,et al.  Hybrid Synchronization of Identical Chaotic Systems Using Sliding Mode Control and an Application to Vaidyanathan Chaotic Systems , 2015, Advances and Applications in Sliding Mode Control Systems.

[7]  Hussein A. Abbass,et al.  MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[8]  Michael G. Strintzis,et al.  3D facial expression recognition using swarm intelligence , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Ahmad Taher Azar,et al.  Neuro-fuzzy feature selection approach based on linguistic hedges for medical diagnosis , 2014, Int. J. Model. Identif. Control..

[10]  Sanjay Kumar Singh,et al.  Black Hole Algorithm and Its Applications , 2015, Computational Intelligence Applications in Modeling and Control.

[11]  Ajith Abraham,et al.  Particle Swarm Optimization: Performance Tuning and Empirical Analysis , 2009, Foundations of Computational Intelligence.

[12]  Ahmad Taher Azar,et al.  A novel hybrid feature selection method based on rough set and improved harmony search , 2015, Neural Computing and Applications.

[13]  Ahmad Taher Azar,et al.  Handbook of Research on Advanced Intelligent Control Engineering and Automation , 2015 .

[14]  Shinn-Ying Ho,et al.  OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[15]  Meng Joo Er,et al.  High-speed face recognition based on discrete cosine transform and RBF neural networks , 2005, IEEE Transactions on Neural Networks.

[16]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[17]  Hongbin Zhang,et al.  Feature selection using tabu search method , 2002, Pattern Recognit..

[18]  Leonardo Vidal Batista,et al.  Face recognition using DCT coefficients selection , 2008, SAC '08.

[19]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Ahmad Taher Azar,et al.  Hybrid Tolerance Rough Set: PSO Based Supervised Feature Selection for Digital Mammogram Images , 2013, Int. J. Fuzzy Syst. Appl..

[21]  Tomasz Kapitaniak,et al.  Continuous control and synchronization in chaotic systems , 1995 .

[22]  Sundarapandian Vaidyanathan,et al.  Chaos Modeling and Control Systems Design , 2014, Chaos Modeling and Control Systems Design.

[23]  Xingsheng Gu,et al.  A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan ☆ , 2008 .

[24]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[25]  Robert M. May,et al.  Simple mathematical models with very complicated dynamics , 1976, Nature.

[26]  Carroll,et al.  Synchronization in chaotic systems. , 1990, Physical review letters.

[27]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[28]  Erwie Zahara,et al.  A hybridized approach to data clustering , 2008, Expert Syst. Appl..

[29]  E. Ozcan,et al.  Particle swarm optimization: surfing the waves , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[30]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[31]  Scott Kirkpatrick,et al.  Optimization by simulated annealing: Quantitative studies , 1984 .

[32]  Mei Fangquan,et al.  Identification of beef cattle by analyzing images of their muzzle patterns lifted on paper. , 2002 .

[33]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[34]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[35]  Ahmad Taher Azar,et al.  PSORR - An unsupervised feature selection technique for fetal heart rate , 2013, 2013 5th International Conference on Modelling, Identification and Control (ICMIC).

[36]  Thomas Stützle,et al.  Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm , 2009, IEEE Transactions on Evolutionary Computation.

[37]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[38]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[39]  Patrick Siarry,et al.  A survey on optimization metaheuristics , 2013, Inf. Sci..

[40]  Aboul Ella Hassanien,et al.  Dimensionality reduction of medical big data using neural-fuzzy classifier , 2014, Soft Computing.

[41]  Sanjay Kumar Singh,et al.  Swarm Intelligence for Biometric Feature Optimization , 2015 .

[42]  Jemal H. Abawajy,et al.  Big Data in Complex Systems: Challenges and Opportunities , 2015 .

[43]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[44]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Leandro dos Santos Coelho,et al.  Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization , 2008, Expert Syst. Appl..

[46]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[47]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[48]  Sundarapandian Vaidyanathan,et al.  Backstepping Controller Design for the Global Chaos Synchronization of Sprott's Jerk Systems , 2015, Chaos Modeling and Control Systems Design.

[49]  A. Baranov,et al.  Breed differences and intra-breed genetic variability of dermatoglyphic pattern of cattle. , 1993, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[50]  Jacek Klinowski,et al.  Taboo Search: An Approach to the Multiple Minima Problem , 1995, Science.

[51]  I ScottKirkpatrick.  Optimization by Simulated Annealing: Quantitative Studies , 1984 .

[52]  Erwie Zahara,et al.  A hybrid genetic algorithm and particle swarm optimization for multimodal functions , 2008, Appl. Soft Comput..

[53]  Martin D. Levine,et al.  Face Recognition Using the Discrete Cosine Transform , 2001, International Journal of Computer Vision.

[54]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[55]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[56]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[57]  Sharath Pankanti,et al.  Biometrics: a grand challenge , 2004, ICPR 2004.

[58]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010 .

[59]  Ming Yu,et al.  New Face Recognition Method Based on DWT/DCT Combined Feature Selection , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[60]  M. Clerc,et al.  Particle Swarm Optimization , 2006 .

[61]  Chukwudi Anyakoha,et al.  A review of particle swarm optimization. Part I: background and development , 2007, Natural Computing.

[62]  Oscar Castillo,et al.  Optimization of type-2 fuzzy systems based on bio-inspired methods: A concise review , 2012, Inf. Sci..

[63]  Sundarapandian Vaidyanathan,et al.  Analysis and Control of a 4-D Novel Hyperchaotic System , 2015, Chaos Modeling and Control Systems Design.

[64]  Dusan Teodorovic,et al.  Bee Colony Optimization (BCO) , 2009, Innovations in Swarm Intelligence.

[65]  Wang Ling.  Survey on Chaotic Optimization Methods , 2001 .

[66]  K. Aihara,et al.  Chaotic neural networks , 1990 .

[67]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[68]  Emile H. L. Aarts,et al.  Global optimization and simulated annealing , 1991, Math. Program..

[69]  A.S. Samra,et al.  Face recognition using wavelet transform, fast Fourier transform and discrete cosine transform , 2003, 2003 46th Midwest Symposium on Circuits and Systems.

[70]  Ajith Abraham,et al.  Swarm Intelligence in Data Mining , 2009, Swarm Intelligence in Data Mining.

[71]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[72]  Ahmad Taher Azar,et al.  Feature selection using swarm-based relative reduct technique for fetal heart rate , 2014, Neural Computing and Applications.

[73]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[74]  Patrick M. Pilarski,et al.  A SWARM-BASED SYSTEM FOR OBJECT RECOGNITION , 2005 .

[75]  Ibrahim Eksin,et al.  A new optimization method: Big Bang-Big Crunch , 2006, Adv. Eng. Softw..

[76]  Bing Li,et al.  Optimizing Complex Functions by Chaos Search , 1998, Cybern. Syst..

[77]  Guanrong Chen,et al.  On robust control of uncertain chaotic systems: a sliding-mode synthesis via chaotic optimization , 2003 .

[78]  Sundarapandian Vaidyanathan,et al.  Anti-synchronization of Identical Chaotic Systems Using Sliding Mode Control and an Application to Vaidyanathan-Madhavan Chaotic Systems , 2015, Advances and Applications in Sliding Mode Control Systems.

[79]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[80]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[81]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..