An Efficient Technique for Size Reduction of Convolutional Neural Networks after Transfer Learning for Scene Recognition Tasks

[1] S. M. Salaken, A. Khosravi, T. Nguyen, and S. Nahavandi, “Extreme learning machine based transfer learning algorithms: A survey,” Neurocomputing, vol. 267, pp. 516–524, 2017. https://doi.org/10.1016/j.neucom.2017.06.03710.1016/j.neucom.2017.06.037Search in Google Scholar

[2] D. Han, Q. Liu, and W. Fan, “A new image classification method using CNN transfer learning and web data augmentation,” Expert Systems with Applications, vol. 95, pp. 43–56, 2018. https://doi.org/10.1016/j.eswa.2017.11.02810.1016/j.eswa.2017.11.028Search in Google Scholar

[3] L. Wang, L. Ge, R. Li, and Y. Fang, “Three-stream CNNs for action recognition,” Pattern Recognition Letters, vol. 92, pp. 33–40, 2017. https://doi.org/10.1016/j.patrec.2017.04.00410.1016/j.patrec.2017.04.004Search in Google Scholar

[4] V. Campos, B. Jou, and X. Giró-i-Nieto, “From pixels to sentiment: Fine-tuning CNNs for visual sentiment prediction,” Image and Vision Computing, vol. 65, pp. 15–22, 2017. https://doi.org/10.1016/j.imavis.2017.01.01110.1016/j.imavis.2017.01.011Search in Google Scholar

[5] L. H. S. Vogado, R. M. S. Veras, F. H. D. Araujo, R. R. V. Silva, and K. R. T. Aires, “Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification,” Engineering Applications of Artificial Intelligence, vol. 72, pp. 415–422, 2018. https://doi.org/10.1016/j.engappai.2018.04.02410.1016/j.engappai.2018.04.024Search in Google Scholar

[6] A. Khatami, M. Babaie, H. R. Tizhoosh, A. Khosravi, T. Nguyen, and S. Nahavandi, “A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval,” Expert Systems with Applications, vol. 100, pp. 224–233, 2018. https://doi.org/10.1016/j.eswa.2018.01.05610.1016/j.eswa.2018.01.056Search in Google Scholar

[7] X. Cheng, J. Lu, J. Feng, B. Yuan, and J. Zhou, “Scene recognition with objectness,” Pattern Recognition, vol. 74, pp. 474–487, 2018. https://doi.org/10.1016/j.patcog.2017.09.02510.1016/j.patcog.2017.09.025Search in Google Scholar

[8] X. Song, S. Jiang, L. Herranz, Y. Kong, and K. Zheng, “Category co-occurrence modeling for large scale scene recognition,” Pattern Recognition, vol. 59, pp. 98–111, 2016. https://doi.org/10.1016/j.patcog.2016.01.01910.1016/j.patcog.2016.01.019Search in Google Scholar

[9] S. Gould, R. Fulton, and D. Koller, “Decomposing a scene into geometric and semantically consistent regions,” Proceedings of 2009 IEEE 12th International Conference on Computer Vision, pp. 1–8, 2009. https://doi.org/10.1109/iccv.2009.545921110.1109/ICCV.2009.5459211Search in Google Scholar

[10] Z. Ding, M. Shao, and Y. Fu, “Incomplete multisource transfer learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 2, pp. 310–323, 2018. https://doi.org/10.1109/TNNLS.2016.261876510.1109/TNNLS.2016.261876528113958Search in Google Scholar

[11] H. Zhao, Q. Liu, and Y. Yang, “Transfer learning with ensemble of multiple feature representations,” Proceedings of 2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), pp. 54–61, 2018. http://doi.ieeecomputersociety.org/10.1109/SERA.2018.847718910.1109/SERA.2018.8477189Search in Google Scholar

[12] H. Azizpour, A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson, “Factors of transferability for a generic ConvNet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 9, pp. 1790–1802, 2016. https://doi.org/10.1109/TPAMI.2015.250022410.1109/TPAMI.2015.250022426584488Search in Google Scholar

[13] S. Bai, and H. Tang, “Softly combining an ensemble of classifiers learned from a single convolutional neural network for scene categorization,” Applied Soft Computing, vol. 67, pp. 183–196, 2018. https://doi.org/10.1016/j.asoc.2018.03.00710.1016/j.asoc.2018.03.007Search in Google Scholar

[14] P. Tang, H. Wang, and S. Kwong, “G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition,” Neurocomputing, vol. 225, pp. 188–197, 2017. https://doi.org/10.1016/j.neucom.2016.11.02310.1016/j.neucom.2016.11.023Search in Google Scholar

[15] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 2, pp. 84–90, 2017. https://doi.org/10.1145/306538610.1145/3065386Search in Google Scholar

[16] C. Wang, J. Yu, and D. Tao, “High-level attributes modeling for indoor scenes classification,” Neurocomputing, vol. 121, pp. 337–343, 2013. https://doi.org/10.1016/j.neucom.2013.05.03210.1016/j.neucom.2013.05.032Search in Google Scholar

[17] S. Bai, “Growing random forest on deep convolutional neural networks for scene categorization,” Expert Systems with Applications, vol. 71, pp. 279–287, 2017. https://doi.org/10.1016/j.eswa.2016.10.03810.1016/j.eswa.2016.10.038Search in Google Scholar

[18] B.-J. Han, and J.-Y. Sim, “Saliency detection for panoramic landscape images of outdoor scenes,” Journal of Visual Communication and Image Representation, vol. 49, pp. 27–37, 2017. https://doi.org/10.1016/j.jvcir.2017.08.00310.1016/j.jvcir.2017.08.003Search in Google Scholar

[19] J.-T. Lee, H.-U. Kim, C. Lee, and C.-S. Kim, “Photographic composition classification and dominant geometric element detection for outdoor scenes,” Journal of Visual Communication and Image Representation, vol. 55, pp. 91–105, 2018. https://doi.org/10.1016/j.jvcir.2018.05.01810.1016/j.jvcir.2018.05.018Search in Google Scholar

[20] B. Liu, S. Gould, and D. Koller, “Single image depth estimation from predicted semantic labels,” Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1253–1260, 2010. https://doi.org/10.1109/cvpr.2010.553982310.1109/CVPR.2010.5539823Search in Google Scholar

[21] V. V. Romanuke, “Appropriate number and allocation of ReLUs in convolutional neural networks,” Research Bulletin of the National Technical University of Ukraine “Kyiv Polytechnic Institute”, no. 1, pp. 69–78, 2017. https://doi.org/10.20535/1810-0546.2017.1.8815610.20535/1810-0546.2017.1.88156Search in Google Scholar

[22] V. Romanuke, “Optimal training parameters and hidden layer neuron number of two-layer perceptron for generalised scaled object classification problem,” Information Technology and Management Science, vol. 18, no. 1, pp. 42–48, 2015. https://doi.org/10.1515/itms-2015-000710.1515/itms-2015-0007Search in Google Scholar

[23] V. V. Romanuke, “Interval uncertainty reduction via division-by-2 dichotomization based on expert estimations for short-termed observations,” Journal of Uncertain Systems, vol. 1, no. 12, pp. 3–21, 2018.Search in Google Scholar

[24] J. Yang, S. Li, and W. Xu, “An iterative transfer learning based classification framework,” Proceedings of 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, 2018. https://doi.org/10.1109/IJCNN.2018.848947110.1109/IJCNN.2018.8489471Search in Google Scholar

[25] X. Liu, Z. Liu, G. Wang, Z. Cai, and H. Zhang, “Ensemble transfer learning algorithm,” IEEE Access, vol. 6, pp. 2389–2396, 2018. https://doi.org/10.1109/ACCESS.2017.278288410.1109/ACCESS.2017.2782884Search in Google Scholar

[26] Y. Liu, D. Yang, and C. Zhang, “Relaxed conditions for convergence analysis of online back-propagation algorithm with regularizer for Sigma-Pi-Sigma neural network,” Neurocomputing, vol. 272, pp. 163–169, 2018. https://doi.org/10.1016/j.neucom.2017.06.05710.1016/j.neucom.2017.06.057Search in Google Scholar