Sh. Fekri-Ershad, “Bark texture classification using improved local ternary patterns and multilayer neural network,” Expert Systems with Applications, vol. 158, pp.113509, 2020.
 M. Wojtech, Bark A Field Guide to Trees of the Northeast,” University Press of New England, 2011.
 Z.K. Huang, C.H. Zheng, J.X. Du, and Y.y. Wan, “Bark classification based on textural features using artificial neural networks,” In: International Symposium on Neural Networks, pp. 355–360. 2006.
 S. Boudra, I. Yahiaoui, and A. Behloul, “A comparison of multi-scale local binary pattern variants for bark image retrieval,” In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 764–775, 2015.
 S. Fiel, and R. Sablatnig, “Automated identification of tree species from images of the bark, leaves or needles,” na, pp. 67-74, 2010.
 A. Bressane, J.A. Roveda, and A.C. Martins, “Statistical analysis of texture in trunk images for biometric identification of tree species,” Environmental monitoring and assessment, vol.187, no. 4, pp. 1-9, 2015.
 S. Boudra, I. Yahiaoui, and A. Behloul, “Statistical radial binary patterns (srbp) for bark texture identification,” In: International conference on advanced concepts for intelligent vision systems, pp. 101–113, 2017.
 T. Le-Viet, V. Truong Hoang, “Local binary pattern based on image gradient for bark image classification,” In: Tenth International Conference on Signal Processing Systems, vol. 11071, p. 110710P ,2019.
 R. Ratajczak, S. Bertrand, C. Crispim-Junior, and L. Tougne, “Efficient bark recognition in the wild,” In: International conference on computer vision theory and applications (VISAPP 2019), 2019.
 D. Misra, C. Crispim-Junior, and L. Tougne, “Patch-based cnn evaluation for bark classification,” In: European Conference on Computer Vision, pp. 197–212, 2020.
 F. Alimoğlu, and E. Alpaydin, “Combining multiple representations for pen-based handwritten digit recognition,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 9, no. 1, pp. 1–12, 2001.
 R. Ebrahimpour, S.A.A.A. Arani, and S. Masoudnia, “Improving combination method of ncl experts using gating network,” Neural Computing and Applications, Vol. 22, No. 1, pp. 95–101, 2013.
 M. Pietikäinen, T. Ojala, and Z. Xu, “Rotation-invariant texture classification using feature distributions,” Pattern recognition, vol. 33, no. 1, pp. 43–52, 2000.
 L.Y. Yang, Z. Qin, and R. Huang, “Design of a multiple classifier system,” In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. no. 04EX826), vol. 5, pp. 3272–3276, 2004.
 L. Armi, E. Abbasi, and J. Zarepour-Ahmadabadi, “Mixture of experts based on ELM with trainable gating network,” arXiv preprint arXiv:2105.11706, 2021.
 L. Armi, E. Abbasi, and J. Zarepour-Ahmadabadi “Texture images classification using improved local quinary pattern and mixture of ELM based experts,” Neural Comput & Applic , pp.1–24, 2021.
 T. Ojala, and M. Pietikäinen, “Harwood, D.: A comparative study of texture measures with classification based on featured distributions,” Pattern recognition, vol. 29, no.1, pp. 51–59, 1996.
 M. Pietikäinen, T. Ojala, and Z. Xu, “Rotation-invariant texture classification using feature distributions,” Pattern recognition, vol. 33, no. 1, pp, 43–52, 2000.
 T. Ojala, M. Pietikainen, and T. Maenpaa, “Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on pattern analysis and machine intelligence, vol. 24, no. 7, pp. 971–987, 2002.
 Z. Guo, L. Zhang, and D. Zhang, "A completed modeling of local binary pattern operator for texture classification," IEEE transactions on image processing, vol. 19, no. 6, pp. 1657- 1663, 2010.
 X. Tan, and B. Triggs, "Enhanced local texture feature sets for face recognition under difficult lighting conditions," International Workshop on Analysis and Modeling of Faces and Gestures, pp. 168-182, 2010.
 J.H. Yuan, H.D. Zhu, Y. Gan, and L. Shang, "Enhanced Local Ternary Pattern for Texture Classification," International Conference on Intelligent Computing, pp. 443-448, 2014.
 Y. Zhao, D.-S. Huang, and W. Jia, “Completed Local Binary Count for Rotation Invariant Texture,” IEEE Trans. on Image Process, vol. 21, no, 10, pp. 4492–4497, 2012.
 L. Nanni, A. Lumini, and S. Brahnam, “Local binary patterns variants as texture descriptors for medical image analysis,” Artificial intelligence in medicine, Vol. 49, No. 2, pp. 117-125, 2010.
 L. Armi, and S. Fekri-Ershad, “Texture image classification based on improved local quinary patterns,” Multimedia Tools and Applications, Vol. 78, No. 14, pp. 18995–19018, 2019.
 R.A. Jacobs, M.I. Jordan, S.J. Nowlan, and G.E. “Hinton, Adaptive mixtures of local experts,” Neural computation, Vol. 3, No. 1, pp. 79–87, 1991.
 G.B. Huang, Q.Y. Zhu, C.K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), Vol. 2, pp. 985–990, 2004.
 G.B. Huang, Q.Y. Zhu, C.K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, Vol. 70, No. 1-3, pp. 489–50,1 2006.
 L. Armi, and S. Fekri-Ershad, “Texture image analysis and texture classification methods - A review,” International Online Journal of Image Processing and Pattern Recognition, Vol. 2, No. 1, pp. 1–29, 2019.
 Y. Song, S. Zhang, B. He, Q. Sha, Y. Shen, T. Yan, R. Nian, and A. Lendasse, “Gaussian derivative models and ensemble extreme learning machine for texture image classification,” Neurocomputing, Vol. 277, pp. 53–64, 2018.
 Y. Cai, X. Liu, Y. Zhang, and Z. Cai, “Hierarchical ensemble of extreme learning machine,” Pattern Recognition Letters, vol. 116, pp. 101–106, 2018.
 S. Masar, “Pattern classification based on ensemble of extreme learning machines,” M.S. thesis, Shiraz University, 2018.
 M. Švab, “Computer-vision-based tree trunk recognition,” Ph.D. thesis, Bsc Thesis, (Mentor: doc. dr. Matej Kristan), Fakulteta za racunalništvo in informatiko, Univerza v Ljubljani, 2010.
 R. Lakmann, “Statistische modellierung von farbtexturen,” Ph.D. thesis, University of Koblenz-Landau, Germany, Fölbach, 1998.
 C. Münzenmayer, H. Volk, C. Küblbeck, K. Spinnler, and T. Wittenberg, “Multi-spectral texture analysis using interplane sum-and difference-histograms,” In: Joint Pattern Recognition Symposium, pp. 42–49, 2002.
 Y. Song, S. Zhang, B. He, Q. Sha, Y. Shen, T. Yan, R. Nian, and A. Lendasse, “Gaussian derivative models and ensemble extreme learning machine for texture image classification,” Neurocomputing, vol. 277, pp. 53–64, 2018.
 S. Bertrand, R.B. Ameur, G. Cerutti, D. Coquin, L. Valet, and L. Tougne, “Bark and leaf fusion systems to improve automatic tree species recognition,” Ecological Informatics, vol. 46, pp. 57–73, 2018.
 S. Boudra, I. Yahiaoui, and A. Behloul, “A set of statistical radial binary patterns for tree species identification based on bark images,” Multimedia tools and applications, (2020).
 R. Polikar, “Ensemble based systems in decision making”. IEEE Circuits and systems magazine, vol. 6, no. 3, pp. 21–45, 2006.