Penggunaan Global Contrast Saliency dan Histogram of Oriented Gradient Sebagai Fitur untuk Klasifikasi Jenis Hewan Mamalia
Article Sidebar
Published:
Mar 21, 2020
Keywords:
Mammal HOG Global Contrast Saliency k-NN
Main Article Content
Abstract
Mammal type can be classified based on the face. Every mammal’s face has a different shape. Histogram of Oriented Gradient (HOG) used to get shape feature from mammal’s face. Before this step, Global Contrast Saliency used to make images focused on an object. This process conducts to get better shape features. Then, classification using k-Nearest Neighbor (k-NN). Euclidean and cityblock distance with k=3,5,7 and 9 used in this study. The result shows cityblock distance with k=9 better than Euclidean distance for each k. Tiger is superior to others for all distances. Sheep is bad classified.
Downloads
Download data is not yet available.
Article Details
How to Cite
Yohannes, Y., & Al Rivan, M. E. (2020). Penggunaan Global Contrast Saliency dan Histogram of Oriented Gradient Sebagai Fitur untuk Klasifikasi Jenis Hewan Mamalia. PETIR, 13(1), 80–85. https://doi.org/10.33322/petir.v13i1.908
Section
Articles
References
[1] M. N. Alli and S. Viriri, “Animal identification based on footprint recognition,” IEEE Int. Conf. Adapt. Sci. Technol. ICAST, 2013.
[2] S. Taheri and Ö. Toygar, “Animal classification using facial images with score-level fusion,” IET Comput. Vis., vol. 12, no. 5, pp. 679–685, 2018.
[3] M. E. Al Rivan and Y. Yohannes, “Klasifikasi Mamalia Berdasarkan Bentuk Wajah Dengan K-NN Menggunakan Fitur CAS Dan HOG,” J. Tek. Inform. dan Sist. Inf., vol. 5, no. 2, pp. 173–180, 2019.
[4] Y. Yohannes, Y. P. Sari, and I. Feristyani, “Klasifikasi Wajah Hewan Mamalia Tampak Depan Menggunakan k-Nearest Neighbor Dengan Ekstraksi Fitur HOG,” J. Tek. Inform. dan Sist. Inf., vol. 5, no. 1, pp. 84–97, 2019.
[5] Z. Cao, J. C. Principe, B. Ouyang, F. Dalgleish, and A. Vuorenkoski, “Marine Animal Classification Using Combined CNN and Hand-designed Image Features,” Ocean. 2015 - MTS/IEEE Washingt., pp. 2–7, 2015.
[6] S. D. Cahyaningtyas, “Pengenalan Wajah Menggunakan Metode Ekstraksi Fitur Local Binary Pattern Berdasarkan Metode K-Nearest Neighbor,” Tek. Inform. Univ. Dian Nuswantoro, 2016.
[7] F. Liantoni and H. Nugroho, “Klasifikasi Daun Herbal Menggunakan Metode Naïve Bayes Classifier Dan K- Nearest Neighbor,” J. Simantec, vol. 5, no. 1, pp. 9–16, 2015.
[8] H. Wijayanto, “Klasifikasi Batik Menggunakan Metode K-Nearest Neighbour Berdasarkan Gray Level Co-Occurrence Matrices (GLCM),” Tek. Inform. FIK UDINUS, 2015.
[9] F. Fandiansyah, J. Y. Sari, and I. P. Ningrum, “Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis Dan K Nearest Neighbor,” J. Inform., vol. 11, no. 2, pp. 48–59, 2017.
[10] M. M. Cheng, N. J. Mitra, X. Huang, P. H. S. Torr, and S. M. Hu, “Global contrast based salient region detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 3, pp. 569–582, 2015.
[11] Z. Si and S. Zhu, “Learning Hybrid Image Templates (HIT) by Information Projection,” IEEE Trans. Pattern Anal. Mach. Intell., pp. 1354–1367, 2012.
[12] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 2005, vol. I, pp. 886–893.
[13] J. P. Jose, P. Poornima, and K. M. Kumar, “A novel method for color face recognition using KNN classifier,” 2012 Int. Conf. Comput. Commun. Appl. ICCCA 2012, 2012.
[2] S. Taheri and Ö. Toygar, “Animal classification using facial images with score-level fusion,” IET Comput. Vis., vol. 12, no. 5, pp. 679–685, 2018.
[3] M. E. Al Rivan and Y. Yohannes, “Klasifikasi Mamalia Berdasarkan Bentuk Wajah Dengan K-NN Menggunakan Fitur CAS Dan HOG,” J. Tek. Inform. dan Sist. Inf., vol. 5, no. 2, pp. 173–180, 2019.
[4] Y. Yohannes, Y. P. Sari, and I. Feristyani, “Klasifikasi Wajah Hewan Mamalia Tampak Depan Menggunakan k-Nearest Neighbor Dengan Ekstraksi Fitur HOG,” J. Tek. Inform. dan Sist. Inf., vol. 5, no. 1, pp. 84–97, 2019.
[5] Z. Cao, J. C. Principe, B. Ouyang, F. Dalgleish, and A. Vuorenkoski, “Marine Animal Classification Using Combined CNN and Hand-designed Image Features,” Ocean. 2015 - MTS/IEEE Washingt., pp. 2–7, 2015.
[6] S. D. Cahyaningtyas, “Pengenalan Wajah Menggunakan Metode Ekstraksi Fitur Local Binary Pattern Berdasarkan Metode K-Nearest Neighbor,” Tek. Inform. Univ. Dian Nuswantoro, 2016.
[7] F. Liantoni and H. Nugroho, “Klasifikasi Daun Herbal Menggunakan Metode Naïve Bayes Classifier Dan K- Nearest Neighbor,” J. Simantec, vol. 5, no. 1, pp. 9–16, 2015.
[8] H. Wijayanto, “Klasifikasi Batik Menggunakan Metode K-Nearest Neighbour Berdasarkan Gray Level Co-Occurrence Matrices (GLCM),” Tek. Inform. FIK UDINUS, 2015.
[9] F. Fandiansyah, J. Y. Sari, and I. P. Ningrum, “Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis Dan K Nearest Neighbor,” J. Inform., vol. 11, no. 2, pp. 48–59, 2017.
[10] M. M. Cheng, N. J. Mitra, X. Huang, P. H. S. Torr, and S. M. Hu, “Global contrast based salient region detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 3, pp. 569–582, 2015.
[11] Z. Si and S. Zhu, “Learning Hybrid Image Templates (HIT) by Information Projection,” IEEE Trans. Pattern Anal. Mach. Intell., pp. 1354–1367, 2012.
[12] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 2005, vol. I, pp. 886–893.
[13] J. P. Jose, P. Poornima, and K. M. Kumar, “A novel method for color face recognition using KNN classifier,” 2012 Int. Conf. Comput. Commun. Appl. ICCCA 2012, 2012.