A Novel Hybrid SVM-CNN Method for Extracting Characteristics and Classifying Cattle Branding

Carlos Silva; Daniel Welfer

Autores/as

Carlos Silva Escuela Politécnica Nacional
Daniel Welfer Universidade Federal de Santa Maria

Palabras clave:

Convolutional Neural Network, Support Vector Machines, Cattle Branding

Resumen

A tool that can perform the automatic identification of cattle brandings is essential for the government agencies responsible for the record, control and inspection of this activity. This article presents a novel hybrid method that uses Convolutional Neural Networks (CNN) to extract features from images and Support Vector Machines (SVM) to classify the brandings. The experiments were performed using a cattle branding image set provided by the City Hall of Bagé, Brazil. Metrics of Overall Accuracy, Recall, Precision, Kappa Coefficient, and Processing Time were used in order to assess the proposed tool. The results obtained here were satisfactory, reaching a Overall Accuracy of 93.11% in the first experiment with 39 brandings and 1,950 sample images, and 95.34% of accuracy in the second experiment, with the same 39 brandings, but with 2,730 sample images. The processing time attained in the experiments was 31.661s and 41.749s, respectively.

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Biografía del autor/a

Carlos Silva, Escuela Politécnica Nacional

Jenny Torres es subdecana (e) de la Facultad de Ingeniería en Sistemas de la Escuela Politécnica Nacional (EPN). Obtuvo su doctorado en Informática en la Universidad Pierre y Marie Curie de Francia. En 2009 obtuvo su M.Sc en Ciencias Computacionales en la Universidad Paris-Est Créteil. Antes de ser becaria de la SENESCYT, culminó una maestría en Gerencia de Redes y Telecomunicaciones en la Escuela Politécnica del Ejército y en 2006 se graduó de ingeniera en Sistemas en la EPN.
Su investigación se centra en seguridad informática, gestión de redes, gestión de identidades, redes inalámbricas e infraestructuras abiertas. Fue docente invitada durante seis meses en la Universidad de Paraná, Curitiba, Brasil y forma parte de los equipos de investigación Phare y NR2 en Francia y Brasil respectivamente.

Referencias

Secretaria do Planejamento e Desenvolvimento Regional Governo do Estado do Rio Grande do Sul, Brasil, http://www.scp.rs.gov.br, accessed 19 july 2015.

R. Arnoni. Os Registros e Catálogos de Marcas de Gado da Região Platina. Pelotas: Revista Memória em Rede da UFPEL, 2013.

G. Sanchez, M. Rodriguez. “Cattle Marks Recognition by Hu and Legendre Invariant Moments”. ARPN Journal of Engineering and Applied Sciences, vol. 11, Nº 1, 2016.

C. Silva, D. Welfer, F.P. Gioda, C. Dornelles. “Cattle Brand Recognition using Convolutional Neural Network and Support Vector Machines”. IEEE Latin America Transactions, vol. 15, Nº 2, 2017. DOI: 10.1109/TLA.2017.7854627.

X.X Niu, C. Y. Suen. “A Novel Hybrid CNN-SVM Classifier for Recognizing Handwritten Digits”. Pattern Recognition, n. 45, p. 1318-1325, 2011. DOI: 10.1016/j.patcog.2011.09.021.

K. Jarret, K. Kavukcuoglu, Y. LeCun. “What Is The Best Multi-Stage Architecture for Object Recognition?”. IEEE 12th International Conference on Computer Vision, p. 2146-2153, 2009.DOI: 10.1109/ICCV.2009.5459469.

G. Juraszek. Reconhecimento de Produtos por Imagem Utilizando Palavras Visuais e Redes Neurais Convolucionais, Joinville: UDESC, 2014.

Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D Jackel. “Handwritten Digit Recognition with a Back-Propagation Network”. In: Advances in Neural Information Processing Systems.[S.l.]: Morgan Kaufmann, p. 396-404, 1990.

D. Ciregan, U. Meier, J. Schmidhuber. “Multi-Columm Deep Neural Networks for Image Classification”. In: Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2012), [S.l.: s.n.]. p. 3642-3649, 2012. DOI: 10.1109/CVPR.2012.6248110.

K. Kavukcuoglu, P. Sermanet, Y. Boreau, K. Gregor, M. Mathieu, Y. LeCun. “Learning Convolutional Feature Hierarchies for Visual Recognition”. In: Advances in Neural Information Processing Systems, ed. by J.D Lafferty and C.K.I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta, vol. 23, p. 1090-1098, 2010.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun. “Overfeat: Integrated Recognition, Localization and Detection Using Convolutional Networks”, CoRR, abs/1312.6229, 2013.

A.S. Razavian, H. Azizpour, J. Sullivan, S. Carlsson. “CNN Features Off-the-Shelf: An Astounding Baseline for Recognition”, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, p. 806-813, 2014. DOI: 10.1109/CVPRW.2014.131.

P. Constante, A. Gordón, O. Chang, E. Pruna, I. Escobar, F. Acuña. “Artificial Vision Techniques for Strawberry's Industrial Classification”.IEEE Latin America Transactions, vol. 14, Nº 6, 2016. DOI: 10.1109/TLA.2016.7555221.

Vlfeat. Biblioteca Open Source VLFeat, http://www.vlfeat.org/matconvnet/models/beta16/imagenet-caffe-alex.mat, accessed 3 june 2016.

Y. LeCun, K. Kavukcuoglu, C. Farabet. “Convolutional Networks and Applications in Vision”. In: Circuits and Systems (ISCAS),Proceedings of 2010 IEEE International Symposiumon. IEEE, p. 253-256, 2010. DOI: 10.1109/ISCAS.2010.5537907.

I. Arel, D. Rose, T. Karnowski. “Deep Machine Learning -A New Frontier in Artificial Intellingence Research [research frontier]”. Computational Intelligence Magazine, IEEE, v. 5, n. 4, p. 13-18. ISSN 1556-603X, 2010. DOI: 10.1109/MCI.2010.938364.

A. Tchangani. “Support Vector Machines: A Tool for Pattern Recognition and Classification”. Studies in Informatics & Control Journal, 14: 2. 99-109, 2005.

J. Landis, G. Koch. “The Measurement of Observer Agreement for Categorical Data”. International Biometric Society, v.33 n.1, p. 159, 1977. DOI: 10.2307/2529310.

Y. Bengio, A. Courville, V. Vincent. “Representation learning: A review and new perspectives”. IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 35, n. 8, p. 1798–1828, 2013. DOI: 10.1109/TPAMI.2013.50.

G. Hinton. “To Recognize Shapes First Learn to Generate Images”. Progress in Brain Research. Elsevier, v. 165, p. 535-547, 2007. DOI: 10.1016/S0079-6123(06)65034-6.

M. Zeiler, R. Fergus. “Visualizing and Understanding Convolutional Networks”. European Conference on Computer Vision. Springer, p. 818 –833, 2014. DOI: 10.1007/978-3-319-10590-1_53.