Segmentation and detection of cattle branding images using CNN and SVM classification
Abstract This article presents a hybrid method that uses Convolutional Neural Networks (CNN) to segmentation and Support Vector Machines (SVM) to detection the brandings. The experiments were performed using a cattle branding images. 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% in the first experiment with 39 brandings and 1,950 sample images, and 95% 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 32s and 42s, respectively.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Atlas Socioeconômico, Brasil, https://atlassocioeconomico.rs.gov.br/bovinos, accessed 8 march 2019.
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 Symposium on. 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.
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 Symposium on. 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.
Silva, C., Weber, J., & Belloni, B. (2019). Segmentation and detection of cattle branding images using CNN and SVM classification. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(2), 19–32. https://doi.org/10.14201/ADCAIJ2019821932
Downloads
Download data is not yet available.
+
−