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Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm

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Abstract

Animal biometrics is an emerging research field that develops quantified methodologies for representing and detecting the visual appearances of animal based on generic features and primary biometric characteristics. The identification of individual cattle is an important issue for classification of different breads, their registration, traceability, health management, and verification of false insurance claim throughout the world. To solve these major problems, the muzzle (nose) point image pattern of cattle is a suitable and primary biometric characteristic for the recognition of cattle. The recognition of muzzle point images is similar to the recognition of minutiae points in the human fingerprints. In this paper, we propose a hybrid feature extraction approach for the automatic recognition and classification of cattle breeds based on captured muzzle point image pattern features using low- cost camera. The major contributions of this research is based on following aspects: (1) preparation of muzzle point image dataset, (2) extraction of salient set of features and (3) K-nearest neighbour (K-NN), Fuzzy-KNN, Decision Tree (DT), Gaussian Mixture Model (GMM), Probabilistic Neural Network (PNN), Multilayer Perceptron (MLP) and Naive Bayes classification models are applied to recognition, and classification of individual cattle using their muzzle point pattern. This paper, therefore, demonstrates the automatic recognition and classification of livestock using the set of extracted muzzle point image features. The experimental results show that proposed hybrid feature extraction and recognition approach outperforms the current state-of- the art method for the identification of individual cattle using their muzzle point image pattern.

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References

  1. Ahmed S, Gaber T, Tharwat A, Hassanien AE, Snael V, (2015) Muzzle-based cattle identification using speed up robust feature approach, in: Proceedings of 2015 International Conference on Intelligent Networking and Collaborative Systems (INCOS), pp. 99–104

  2. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  MATH  Google Scholar 

  3. Allen A, Golden B, Taylor M, Patterson D, Henriksen D, Skuce R (2008) Evaluation of retinal imaging technology for the biometric identification of bovine animals in northern Ireland. Livest Sci 116(1):42–52

    Article  Google Scholar 

  4. Awad AI, Zawbaa HM, Mahmoud HA, Nabi EHHA, Fayed RH, Hassanien AE, (2013) A robust cattle identification scheme using muzzle print images, in: Proceedings of 2013 I.E. Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 529–534

  5. Baranov A, Graml R, Pirchner F, Schmid D (1993) Breed differences and intra-breed genetic variability of dermatoglyphic pattern of cattle. J Anim Breed Genet 110(1–6):385–392

    Article  Google Scholar 

  6. Barry B, Gonzales-Barron U, McDonnell K, Butler F, Ward S (2007) Using muzzle pattern recognition as a biometric approach for cattle identification. Trans ASABE 50(3):1073–1080

    Article  Google Scholar 

  7. Bishop CM., Pattern recognition, Machine Learning

  8. Boughrara H, Chtourou M, Ben Amar C, Chen L (2014) Facial expression recognition based on a mlp neural network using constructive training algorithm. Multimedia Tools and Applications 75(2):709–731

    Article  Google Scholar 

  9. Cai C, Li J, (2013) Cattle face recognition using local binary pattern descriptor, in: Proceedings of 2013 I.E. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 1–4

  10. Chen K, Wang H, Jiang M (2014) Li, skin color modeling for face detection and segmentation: a review and a new approach. Multimedia Tools and Applications 75(2):839–862

    Article  Google Scholar 

  11. Corkery G, Gonzales-Barron UA, Butler F, Mc Donnell K, Ward S (2007) A preliminary investigation on face recognition as a biometric identifier of sheep. Trans ASABE 50(1):313–320

    Article  Google Scholar 

  12. Dalal N., Triggs B., (2005) Histograms of oriented gradients for human detection, in: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 (CVPR), Vol. 1, pp. 886–893

  13. Davis LS, Johns SA, Aggarwal JK (1979) Texture analysis using generalized co-occurrence matrices. IEEE Trans Pattern Anal Mach Intell PAMI-1(3):251–259

    Article  Google Scholar 

  14. de Wouwer GV, Scheunders P, Dyck DV (1999) Statistical texture characterization from discrete wavelet representations. IEEE Trans Image Process 8(4):592–598

    Article  Google Scholar 

  15. Denoeux T (1995) A k-nearest neighbor classification rule based on dempster-Shafer theory. IEEE Transactions on Systems, Man, and Cybernetics 25(5):804–813

    Article  Google Scholar 

  16. Duyck J, Finn C, Hutcheon A, Vera P, Salas J, Ravela S (2015) Sloop: A pattern retrieval engine for individual animal identification. Pattern Recogn 48(4):1059–1073

    Article  Google Scholar 

  17. E. R. No, 178/2002 of the European parliament and of the council of 28 January 2002 laying down the general principles and requirements of food law, establishing the European food safety authority and laying down procedures in matters of food safety, Official Journal of the European Communities 1

  18. Eradus WJ, Jansen MB (1999) Animal identification and monitoring. Comput Electron Agric 24(1):91–98

    Article  Google Scholar 

  19. Frucci M, Nappi M, Riccio D, di Baja Wire GS (2016) Watershed based iris recognition. Pattern Recogn 52:148–159

    Article  Google Scholar 

  20. Gaber T, Tharwat A, Hassanien AE, Snasel V (2016) Biometric cattle identification approach based on webers local descriptor and adaboost classifier. Comput Electron Agric 122:55–66

    Article  Google Scholar 

  21. Garcia-Pedrajas N., del Castillo JAR., Cerruela-Garcia G., (2016) A proposal for local k values for k-nearest neighbor rule, IEEE Transactions on Neural Networks and Learning Systems PP (99) 1–6

  22. Goon AM., Dasgupta B., Gupta M., (1963) Fundamentals of statistics, World Press Private Limited

  23. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804

    Article  Google Scholar 

  24. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(6):610–621

    Article  Google Scholar 

  25. Iakovidis DK., Keramidas EG., Maroulis D., (2008) Fuzzy local binary patterns for ultrasound texture characterization, in: Image analysis and recognition, pp. 750–759

  26. Jain A, Flynn P, Ross AA (2007) Handbook of biometrics. Springer Science & Business Media, Newyork

    Google Scholar 

  27. Johnston A, Edwards D (1996) Welfare implications of identification of cattle by ear tags. The Veterinary Record 138(25):612–614

    Article  Google Scholar 

  28. K¨uhl HS, Burghardt T (2013) Animal biometrics: quantifying and detecting phenotypic appearance. Trends Ecol Evol 28(7):432–441

    Article  Google Scholar 

  29. Kim HT, Ikeda Y, Choi HL (2005) The identification of japanese black cattle by their faces. Asian Australasian Journal of Animal Sciences 18(6):868–872

    Article  Google Scholar 

  30. Kim S-H, An K-J, Jang S-W, Kim G-Y (2016) Texture feature-based text region segmentation in social multimedia data. Multimedia Tools and Applications:1–15

  31. Koniar D, Hargaˇs L, Loncov’a Z, Duchoˇn F, Beˇno P (2015) Machine vision application in animal trajectory tracking. Comput Methods Prog Biomed 127:258–272

    Article  Google Scholar 

  32. Krishnan MMR, Venkatraghavan V, Acharya UR, Pal M, Paul RR, Min LC, Ray AK, Chatterjee J, Chakraborty C (2012) Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm. Micron 43(2):352–364

    Article  Google Scholar 

  33. Kumar S, Tiwari S, Singh SK, (2015) Face recognition for cattle, in: 2015 Third IEEE International Conference on Image Information Processing (ICIIP), pp. 65–72

  34. Kumar S, Tiwari S, Singh SK (2016) Face recognition of cattle: can it be done? Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 86(2):137–148

    Article  Google Scholar 

  35. Lahiri M, Tantipathananandh C, Warungu R, Rubenstein DI, Berger-Wolf TY, (2011) Biometric animal databases from field photographs: Identification of individual zebra in the wild, in: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, ICMR ‘11, pp. 6:1–6:8

  36. Lara López G, Peña Pérez Negrón A, De Antonio Jiménez A, Ramírez Rodríguez J, Imbert Paredes R (2016) Comparative analysis of shape descriptors for 3D objects. Multimedia Tools and Applications 1–48. doi:10.1007/s11042-016-3330-5

  37. Food Safety Authority of Ireland. General Principles of Food Law (2002) Regulation (ec) no. 178/2002 of the European parliament and of the council of 28 January 2002, laying down the general principles and requirements of food law, establishing the European food safety authority, and laying down procedures in matters of food safety, OJ L 31 (1.2). https://www.fsai.ie/uploadedFiles/Legislation/Food_Legisation_Links/General_Principles_of_Food_Law/Reg178_2002.pdf

  38. Law MHC, Figueiredo MAT, Jain AK (2004) Simultaneous feature selection and clustering using mixture models. IEEE Trans Pattern Anal Mach Intell 26(9):1154–1166

    Article  Google Scholar 

  39. Lopatka K, Kotus J, Czyzewski A (2016) Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations. Multimedia Tools and Applications 75(17):10407–10439

    Article  Google Scholar 

  40. Luccheseyz L, Mitray S (2001) Color image segmentation: a state-of-the-art survey. Proceedings of the Indian National Science Academy (INSA-A) 67(2):207–221

    Google Scholar 

  41. Lucena M, Mart’ınez-Carrillo AL, Fuertes JM, Carrascosa F, Ruiz A (2014) Decision support system for classifying archaeological pottery profiles based on mathematical morphology. Multimedia Tools and Applications 75(7):3677–3691

    Article  Google Scholar 

  42. Marchant J, Secure animal identification and source verification, JM Communications, UK. Copyright Optibrand Ltd., LLC

  43. Matiolanski A, Maksimova A, Dziech A (2015) Cctv object detection with fuzzy classification and image enhancement. Multimedia Tools and Applications:1–16. doi:10.1007/s11042-015-2697-z

  44. Mehta R, Eguiazarian KE (2016) Texture classification using dense micro-block difference. IEEE Trans Image Process 25(4):1604–1616

    Article  MathSciNet  Google Scholar 

  45. Minagawa H, Fujimura T, Ichiyanagi M, Tanaka K, Fangquan M, et al., (2002) Identification of beef cattle by analyzing images of their muzzle patterns lifted on paper, in: Proceedings of the 3rd Asian Conference for Information Technology in Asian agricultural information technology & management, pp. 596–600

  46. Mishra S, Tomer OS, Kalm E (1995) Muzzle dermatoglyphics: a new method to identify bovines. Asian Livestock, August 1995, pp 91–96

  47. Murphy KP., Naive bayes classifiers, University of British Columbia

  48. Noviyanto A, Arymurthy AM, (2012) Automatic cattle identification based on muzzle photo using speed-up robust features approach, in: Proceedings of the 3rd European Conference of Computer Science, ECCS, Vol. 110, p. 114

  49. Noviyanto A, Arymurthy AM (2013) Beef cattle identification based on muzzle pattern using a matching refinement technique in the sift method. Comput Electron Agric 99:77–84

    Article  Google Scholar 

  50. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  51. Pal SK, Mitra S (1992) Multilayer perceptron, fuzzy sets, and classification. IEEE Transactions on Neural Networks 3(5):683–697

    Article  Google Scholar 

  52. Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recogn 26(9):1277–1294

    Article  Google Scholar 

  53. Perronnin F., S’anchez J., Mensink T., (2010) Improving the fisher kernel for large-scale image classification, in: Computer Vision–ECCV 2010, pp. 143–156

  54. Petersen W (1922) The identification of the bovine by means of nose-prints. J Dairy Sci 5(3):249–258

    Article  Google Scholar 

  55. Reed TR, Dubuf JH (1993) A review of recent texture segmentation and feature extraction techniques. CVGIP: Image understanding 57(3):359–372

    Article  Google Scholar 

  56. Rizzi A, Berolo AJ, Bonanomi C, Gadia D (2014) Unsupervised digital movie restoration with spatial models of color. Multimedia Tools and Applications 75(7):3747–3765

    Article  Google Scholar 

  57. S’anchez J, Perronnin F, Mensink T, Verbeek J (2013) Image classification with the fisher vector: theory and practice. Int J Comput Vis 105(3):222–245

    Article  MathSciNet  MATH  Google Scholar 

  58. Sklansky J (1978) Image segmentation and feature extraction. IEEE Transactions on Systems, Man, and Cybernetics 8(4):237–247

    Article  Google Scholar 

  59. Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118

    Article  Google Scholar 

  60. Sun Y, Jia H, Hu Y, Yin B (2013) Color face recognition based on color image correlation similarity discriminant model. Multimedia Tools and Applications 73(3):2063–2079

    Article  Google Scholar 

  61. Szwoch G (2014) Extraction of stable foreground image regions for unattended luggage detection. Multimedia Tools and Applications 75(2):761–786

    Article  Google Scholar 

  62. Tharwat A, Gaber T, Hassanien AE, (2014) Cattle identification based on muzzle images using gabor features and svm classifier, in: Proceedings of Advanced Machine Learning Technologies and Applications, pp. 236–247

  63. Vlad M, Parvulet RA, Vlad MS, (2012) A survey of livestock identification systems, in: Proceedings of the 13th WSEAS International Conference on Automation and Information (ICAI), pp. 165–170

  64. Wang Z, Fu Z, Chen W, Hu J, (2010) A rfid-based traceability system for cattle breeding in china, in: Proceedings of 2010 I.E. International Conference on Computer Application and System Modeling (ICCASM), Vol. 2, pp. V2–567

  65. Wang X-Y, Zhang B-B, Yang H-Y (2012) Content-based image retrieval by integrating color and texture features. Multimedia Tools and Applications 68(3):545–569

    Article  Google Scholar 

  66. Wang D, Song H, Tie Z, Zhang W, He D (2015) Recognition and localization of occluded apples using kmeans clustering algorithm and convex hull theory: a comparison. Multimedia Tools and Applications 75(6):3177–3198

    Article  Google Scholar 

  67. Wardrope D, Problems [suppurating wounds] with the use of ear tags in cattle. [correspondence], Veterinary Record (United Kingdom)

  68. Werghi N, Berretti S, del Bimbo A (2015) The mesh-lbp: a framework for extracting local binary patterns from discrete manifolds. IEEE Trans Image Process 24(1):220–235

    Article  MathSciNet  Google Scholar 

  69. Xu P, Miao Q, Tang X, Zhang J (2014) A denoising algorithm via wiener filtering in the shearlet domain. Multimedia Tools and Applications 71(3):1529–1558

    Article  Google Scholar 

  70. Yang H, Hou X (2014) Texture segmentation using image decomposition and local self-similarity of different features. Multimedia Tools and Applications 74(15):6069–6089

    Article  Google Scholar 

  71. Ying C, Qi-Guang MIAO, Jia-Chen LIU, Lin GAO (2013) Advance and prospects of AdaBoost algorithm. Acta Automat Sin 39(6):745–758

    Article  Google Scholar 

  72. Zhang J, Niu Y, Nie H (2009) Web document classification based on fuzzy k-nn algorithm. Proceedings of International Conference on Computational Intelligence and Security, 2009 CIS ‘09 1:193–196

    Article  Google Scholar 

  73. Zhao Y, Georganas ND, Petriu EM, (2010) Applying contrast-limited adaptive histogram equalization an integral projection for facial feature enhancement and detection, in: Proceedings of 2010 I.E. Conference on Instrumentation and Measurement Technology (I2MTC), pp. 861–866

  74. Zhao Y, Zhao X, Luo R, Liu Y (2015) Person re-identification by encoding free energy feature maps. Multimedia Tools and Applications 75(8):4795–4813

    Article  Google Scholar 

  75. Zhu W, Huang W, Lin Z, Yang Y, Huang S, Zhou J (2015) Data and feature mixed ensemble based extreme learning machine for medical object detection and segmentation. Multimedia Tools and Application 75(5):2815–2837

    Article  Google Scholar 

  76. Zuiderveld K, (1994) Graphics gems iv, 1994, Ch. Contrast Limited Adaptive Histogram Equalization, pp. 474–485

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  1. Department of Computer Science and Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, -221005, India

    Santosh Kumar & Sanjay Kumar Singh

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  1. Santosh Kumar
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Kumar, S., Singh, S.K. Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm. Multimed Tools Appl 76, 26551–26580 (2017). https://doi.org/10.1007/s11042-016-4181-9

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