Local receptive fields based extreme learning machine with hybrid filter kernels for image classification

Bencherif, M. A., Bazi, Y., Guessoum, A., Alajlan, N., Melgani, F., & AlHichri, H. (2015). Fusion of extreme learning machine and graph-based optimization methods for active classification of remote sensing images. IEEE Geoscience and Remote Sensing Letters, 12(3), 527–531.

Chang, S.-Y. & Morgan, N. (2014). Robust CNN-based speech recognition with Gabor filter kernels. In Fifteenth annual conference of the international speech communication association.

Coates, A., Ng, A. & Lee, H. (2011). An analysis of single-layer networks in unsupervised feature learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 215–223.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

Cui, X., Goel, V., & Kingsbury, B. (2015). Data augmentation for deep neural network acoustic modeling. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(9), 1469–1477.

Fogel, I., & Sagi, D. (1989). Gabor filters as texture discriminator. Biological Cybernetics, 61(2), 103–113.

Georghiades, A., Belhumeur, P. & Kriegman, D. (1997). Yale face database. Center for Computational Vision and Control at Yale University, 2, 6. http://cvc.yale.edu/projects/yalefaces/yalefa .

Haralick, R. M., Shanmugam, K., et al. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621.

Huang, G.-B., Bai, Z., Kasun, L. L. C., & Vong, C. M. (2015). Local receptive fields based extreme learning machine. IEEE Computational Intelligence Magazine, 10(2), 18–29.

Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics Part B (Cybernetics), 42(2), 513–529.

Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501.

Huang, J., Yu, Z. L., Cai, Z., Gu, Z., Cai, Z., Gao, W., et al. (2017). Extreme learning machine with multi-scale local receptive fields for texture classification. Multidimensional Systems and Signal Processing, 28(3), 995–1011.

Jain, A. K., & Farrokhnia, F. (1991). Unsupervised texture segmentation using gabor filters. Pattern Recognition, 24(12), 1167–1186.

Jain, A. K., Ratha, N. K., & Lakshmanan, S. (1997). Object detection using gabor filters. Pattern Recognition, 30(2), 295–309.

Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp. 1097–1105.

LeCun, Y., Bengio, Y., et al. (1995). Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, 3361(10), 1995.

LeCun, Y., Huang, F. J. & Bottou, L. (2004a). Learning methods for generic object recognition with invariance to pose and lighting. In Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004, Vol. 2, IEEE, pp. II–104.

LeCun, Y., Huang, F. J. & Bottou, L. (2004b). Learning methods for generic object recognition with invariance to pose and lighting. In Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004, Vol. 2, IEEE, pp. II–104.

LeCun, Y., Huang, F. J. & Bottou, L. (2004c). Learning methods for generic object recognition with invariance to pose and lighting. In Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004, Vol. 2, IEEE, pp. II–104.

Li, W., Chen, C., Su, H., & Du, Q. (2015). Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3681–3693.

Manjunath, B. S., & Ma, W.-Y. (1996). Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8), 837–842.

Mehrotra, R., Namuduri, K. R., & Ranganathan, N. (1992). Gabor filter-based edge detection. Pattern Recognition, 25(12), 1479–1494.

Nair, V. & Hinton, G. E. (2009). 3d object recognition with deep belief nets. In Advances in neural information processing systems, pp. 1339–1347.

Ngiam, J., Chen, Z., Chia, D., Koh, P. W., Le, Q. V. & Ng, A. Y. (2010). Tiled convolutional neural networks. In Advances in neural information processing systems, pp. 1279–1287.

Ojala, T., Maenpaa, T., Pietikainen, M., Viertola, J., Kyllonen, J. & Huovinen, S. (2002). Outex-new framework for empirical evaluation of texture analysis algorithms. In Proceedings of 16th international conference on pattern recognition, 2002, Vol. 1, IEEE, pp. 701–706.

Pietikäinen, M. (2010). Local binary patterns. Scholarpedia, 5(3), 9775.

Rajadell, O., García-Sevilla, P., & Pla, F. (2013). Spectral-spatial pixel characterization using gabor filters for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 10(4), 860–864.

Reininghaus, J., Huber, S., Bauer, U. & Kwitt, R. (2015). A stable multi-scale kernel for topological machine learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4741–4748.

Samaria, F. S. & Harter, A. C. (1994). Parameterisation of a stochastic model for human face identification. In Proceedings of the second IEEE workshop on applications of computer vision, 1994, IEEE, pp. 138–142.

Saxe, A. M., Koh, P. W., Chen, Z., Bhand, M., Suresh, B. & Ng, A. Y. (2011). On random weights and unsupervised feature learning. In ICML, pp. 1089–1096.

Xu, Y., Zhang, B., & Zhong, Z. (2015). Multiple representations and sparse representation for image classification. Pattern Recognition Letters, 68, 9–14.

Yang, P., Zhang, F. & Yang, G. (2018). Fusing DTCWT and LBP based features for rotation, illumination and scale invariant texture classification. In IEEE access.

Zeng, Y., Xu, X., Shen, D., Fang, Y., & Xiao, Z. (2017). Traffic sign recognition using kernel extreme learning machines with deep perceptual features. IEEE Transactions on Intelligent Transportation Systems, 18(6), 1647–1653.

Zhang, S., He, B., Nian, R., Wang, J., Han, B., Lendasse, A., et al. (2014). Fast image recognition based on independent component analysis and extreme learning machine. Cognitive Computation, 6(3), 405–422.