Object-oriented and multi-scale target classification and recognition based on hierarchical ensemble learning

Computers and Electrical Engineering

Volumn 62, Issue , 2017, Pages 538-554

  Liu, Yang ^a   Zheng, Feng bin ^a  

^a HENAN UNIVERSITY   (China)

Author keywords

Brain inspired computing; Deep spiking convolutional neural network; Hierarchical latent Dirichlet allocation; High resolution remote sensing image; Multimedia neural cognitive computing; Target classification and recognition

Indexed keywords

BRAIN; CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DEEP LEARNING; DEEP NEURAL NETWORKS; REINFORCEMENT LEARNING; REMOTE SENSING; STATISTICS; SYNTHETIC APERTURES;

BRAIN-INSPIRED COMPUTING; COGNITIVE COMPUTING; HIGH RESOLUTION REMOTE SENSING IMAGES; LATENT DIRICHLET ALLOCATION; TARGET CLASSIFICATION;

LEARNING ALGORITHMS;

EID: 85009971764     PISSN: 00457906     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compeleceng.2016.12.026     Document Type: Article

References (27)

1. Li, Y.J., Lu, H.M., Li, J.R., Li, X., Li, Y., Serikawa, S., Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 2016:54 (2016), 68–77.
2. Lu, C.Y., Zou, H.X., Sun, H., Zhou, S.L., Combing rough set and RBF neural network for large-scale ship recognition in optical satellite images. Iop conference series: earth and environmental science, 17, 2014, 1–6.
3. Gong, J.L., Fan, G.L., Yu, L.J., Joseph, P.H., Chen, D.R., Fan, N.J., Joint target tracking, recognition and segmentation for infrared imagery using a shape manifold-based level set. Sensors 14:6 (2014), 10124–10145.
4. Li, L., Ren, Y.M., Infrared target recognition based on combined feature and improved Adaboost algorithm. Adv Comput Sci Intell Syst Environ 2 (2011), 707–712.
5. Cui, Z.Y., Cao, Z.J., Yang, J.Y., Ren, H.L., Hierarchical recognition system for target recognition from sparse representations. Math Probl Eng, 2015, 1–6.
6. Xing, X., Ji, K., Zou, H., Sun, J.X., Sparse representation based SAR vehicle recognition along with aspect angle. Sci World J, 2014(2014), 2014 Article ID:834140.
7. Lang, H.T., Zhang, J., Zhang, T., Zhao, D., Meng, J.M., Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery. J Appl Remote Sensing 8:1 (2014), 83623–83630.
8. Isaacs, J.C., Tucker, J., Signal diffusion features for automatic target recognition in synthetic aperture sonar. Digital signal processing workshop and IEEE signal processing education workshop (DSP/SPE), IEEE. Sedona, AZ, 2011, 461–465.
9. Fei, T., Kraus, D., Zoubir, A.M., Contributions to automatic target recognition systems for underwater mine classification. IEEE Trans Geosci Remote Sensing 53:1 (2015), 505–518.
10. Lecun, Y., Bengio, Y., Hinton, G.E., Deep learning. Nature 521:7553 (2015), 436–444.
11. Alex, K., Ilya, S., Hinton, G.E., Imagenet classification with deep convolutional neural networks. Proceedings of the advances in neural information processing systems, 25, 2012 F,.
12. Sun, Y., Wang, X., Tang, X., Deep learning face representation from predicting 10,000 classes. Proceedings of the Computer Vision and Pattern Recognition (CVPR), 2014 IEEE conference on, F, 2014.
13. Wolf, L., Deepface: closing the gap to human-level performance in face verification. Proceedings of the Computer Vision and Pattern Recognition (CVPR), 2014 IEEE conference on IEEE, F, 2014.
14. Blei, D.M., Jordan, M.I., Griffiths, T.L., Tenenbaum, J.B., Hierarchical topic models and the nested Chinese restaurant process. NIPS, 57, 2003, 2003.
15. Salakhutdinov, R., Tenenbaum, J.B., Torralba, A., Learning with hierarchical-deep models. IEEE Trans Pattern Anal Mach Intell 35:8 (2013), 1958–1971.
16. Wan, L., Zhu, L., Fergus, R., A Hybrid neural network-latent topic model. The 15th international conference on Artificial Intelligence and Statistics (AISTATS), La Palma, Canary Islands; JMLR: W&CP 22, 2012, 1287–1294.
17. Liu, Y., Fu, Z.Y., Zheng, F.B., Scene classification of high-resolution remote sensing image based on multimedia neural cognitive computing. Syst Eng Electron 37:11 (2015), 2623–2633.
18. Liu, Y., Tu, C.L., Zheng, F.B., Research of neural cognitive computing model for visual and auditory cross-media retrieval. Comput Sci 42:3 (2015), 19–25 30.
19. Goodfellow, I.J., Wardefarley, D., Mirza, M., Courville, A., Bengio, Y., Maxout networks. International conference on machine learning, 2013 arXiv preprint arXiv:13024389.
20. Glorot, X., Bordes, A., Bengio, Y., Deep sparse rectifier neural networks. Proceedings of the international conference on Artificial Intelligence and Statistics, F, 2011.
21. Wan, L., Zeiler, M.D., Zhang, S., Cun, Y.L., Fergus, R., Regularization of neural networks using DropConnect. Proceedings of the 30th International Conference on Machine Learning (ICML-13), F, 2013.
22. Diehl, P.U., Neil, D., Binas, J., Cook, M., Liu, S., Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. Proceedings of the Neural Networks (IJCNN), 2015 international joint conference on, 2015 F 12-17 July 2015.
23. Cao, Y.Q., Chen, Y., Khosla, D., Spiking deep convolutional neural networks for energy-efficient object recognition. Int J Comput Vis 113:1 (2015), 54–66.
24. Paisley, J., Wang, C., Blei, D.M., Jordan, M.I., Nested hierarchical Dirichlet processes. IEEE Trans Pattern Anal Mach Intell 37:2 (2015), 256–270.
25. Liu, Y., Yang, W., Zheng, F.B., Cognitive neural mechanisms and saliency computational model of visual selective attention. J Chin Comput Syst 35:3 (2014), 584–589.
26. He, K., Zhang, X., Ren, S., Sun, J., Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37:9 (2015), 1904–1916.
27. Cui, Z., Cao, Z., Yang, J., Feng, J., A hierarchical propelled fusion strategy for SAR automatic target recognition. Eurasip J Wireless Commun Netw 2013:1 (2013), 1–8.