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Nowadays, image recognition has become a highly active research topic in cognitive computation community, due to its many potential applications. Generally, the image recognition task involves two subtasks: image representation and image classification. Most feature extraction approaches for image representation developed so far regard independent component analysis (ICA) as one of the essential means. However, ICA has been hampered by its extremely expensive computational cost in real-time implementation. To address this problem, a fast cognitive computational scheme for image recognition is presented in this paper, which combines ICA and the extreme learning machine (ELM) algorithm. It tries to solve the image recognition problem at a much faster speed by using ELM not only in image classification but also in feature extraction for image representation. As an example, our proposed approach is applied to the face image recognition with detailed analysis. Firstly, common feature hypothesis is introduced to extract the common visual features from universal images by the traditional ICA model in the offline recognition process, and then ELM is used to simulate ICA for the purpose of facial feature extraction in the online recognition process. Lastly, the resulting independent feature representation of the face images extracted by ELM rather than ICA will be fed into the ELM classifier, which is composed of numerous single hidden layer feed-forward networks. Experimental results on Yale face database and MNIST digit database have shown the good performance of our proposed approach, which could be comparable to the state-of-the-art techniques at a much faster speed.
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Taylor JG. Cognitive computation. Cogn Comput. 2009;1(1):4–16. CrossRef
Cambria E, Hussain A. Sentic Computing: Techniques, Tools, and Applications. Springer Briefs in Cogn Comput. Dordrecht, Netherlands: Springer 2012.
Wang QF, Cambria E, Liu CL, Hussain A. Common sense knowledge for handwritten Chinese recognition. Cogn Comput. 2013;5(2):234–42. CrossRef
Rakover SŚ, Cahlon B. Face recognition: cognitive and computational processes. Amsterdam: John Benjamins; 2001. CrossRef
Jain AK, Duin RPW, Mao J. Statistical pattern recognition: a review. IEEE Trans Pattern Anal. 2000;22:4–37. CrossRef
Zhao W, Chellappa R, Phillips PJ, Rosenfeld A. Face recognition: a literature survey. ACM Comput Surv. 2003;35:399–458. CrossRef
Yambor WS. Analysis of PCA-based and Fisher discriminant-based image recognition algorithms. Master Thesis, Colorado State University, 2000.
Shakhnarovich G, Moghaddam B. Face recognition in subspaces. In handbook of face recognition. New York: Springer; 2011.
Wang X, Tang X. A unified framework for subspace face recognition. IEEE Trans Pattern Anal. 2004;26:1222–8. CrossRef
Vasilescu MAO, Terzopoulos D. Multilinear subspace analysis of image ensembles. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2003, p. II-93–99.
Brunelli R, Poggio T. Face recognition: features versus templates. IEEE Trans Pattern Anal. 1993;15:1042–52. CrossRef
Liu C, Wechsler H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process. 2002;1:467–76.
Hjelmås E. Feature-based face recognition. In: Proceedings norwegian image processing and pattern recognition conference, 2000.
Jutten C, Herault J. Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture. Signal Process. 1991;24:1–10. CrossRef
Comon P. Independent component analysis, a new concept. Signal Process. 1994;36:287–314. CrossRef
Te-Won L. Independent component analysis, theory and applications. Boston: Kluwer Academic; 1998.
Jolliffe IT. Principal component analysis. New York: Springer; 2002.
Shlens J. A tutorial on principal component analysis. In: Technical report, systems neurobiology laboratory, University of California at San Diego; 2005.
Zou H, Hastie T, Tibshirani R. Sparse principal component analysis. J Comput Graph Stat. 2006;15:265–86. CrossRef
Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing. 2006;70:489–501. CrossRef
Huang GB, Wang DH, Lan Y. Extreme learning machines: a survey. Int J Mach Learn Cybern. 2011;2:107–22. CrossRef
Huang GB, Zhu QY, Siew CK. Extreme learning machine: a new learning scheme of feed forward neural networks. In: Proc. Int. Joint Conf. Neural Netw. (IJCNN2004), Budapest, Hungary; 2004: 985–990.
Huang GB, Chen L. Convex incremental extreme learning machine. Neurocomputing. 2007;70:3056–62. CrossRef
Huang GB, Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing. 2008;71:3460–8. CrossRef
Huang GB, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern. 2012;42(2):513–29. CrossRef
Huang GB, Li MB, Chen L, Siew CK. Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing. 2008;71:576–83. CrossRef
Zhao Z, Liu B, Li W. Image classification based on extreme learning machine. IEIT J Adapt Dynam Comput. 2012;1:5–11. CrossRef
Zhao J, Zhou Z, Cao F. Human face recognition based on ensemble of polyharmonic extreme learning machine. Neural Comput Appl. 2013. doi: 10.1007/s00521-013-1356-4.
Marques I, Graña M. Face recognition with lattice independent component analysis and extreme learning machines. Soft Comput. 2012;16:1525–37. CrossRef
Field DJ. What is the goal of sensory coding? Neural Comput. 1994;6:559–601. CrossRef
Bartlett MS. Face image analysis by unsupervised learning. Dordrecht: Kluwer Academic; 2001. CrossRef
Barlow HB. Unsupervised learning. Neural Comput. 1989;1:295–311. CrossRef
Hyvärinen A, Oja E. A fast fixed-point algorithm for independent component analysis. Neural Comput. 1997;9:1483–92. CrossRef
Hansen LK, Salamon P. Neural network ensemble. IEEE Trans Pattern Anal Mach Intell. 1990;12(10):993–1001. CrossRef
Cai D, He X, Hu Y, Han J, Huang T. Learning a spatially smooth subspace for face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2007: 1–7.
Mankiewicz R. The story of mathematics. Princeton: Princeton Univ. Press; 2004.
Chow SL. Statistical significance: rationale, validity and utility. London, UK: Sage; 1996.
- Fast Image Recognition Based on Independent Component Analysis and Extreme Learning Machine
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