NMF-Based Image Quality Assessment Using Extreme Learning Machine

IEEE Transactions on Cybernetics

Volumn 47, Issue 1, 2017, Pages 232-243

  Wang, Shuigen ^a   Deng, Chenwei ^a   Lin, Weisi ^b   Huang, Guang Bin ^c   Zhao, Baojun ^a  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Extreme learning machine (ELM); human visual system (HVS); image quality assessment (IQA); non negative matrix factorization (NMF)

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; FACTORIZATION; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; MATRIX ALGEBRA;

EXTREME LEARNING MACHINE; IMAGE QUALITY ASSESSMENT; MACHINE LEARNING TECHNIQUES; NONNEGATIVE MATRIX FACTORIZATION; PERCEPTUAL IMAGE QUALITY; PERCEPTUAL VISUAL QUALITY; STATE-OF-THE-ART APPROACH; SUPPORT VECTOR REGRESSION (SVR);

IMAGE QUALITY;

EID: 84957683113     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCYB.2015.2512852     Document Type: Article

References (51)

1. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr. 2004.
2. S. S. Channappayya, A. C. Bovik, and R. W. Heath, "Rate bounds on SSIM index of quantized images," IEEE Trans. Image Process., vol. 17, no. 9, pp. 1624-1639, Sep. 2008.
3. Methodology for the Subjective Assessment of the Quality of Television Pictures, IEEE Standard ITU-R BT.500-11, Int. Telecommun. Union, Geneva, Switzerland, Jun. 2002.
4. Z. Wang, E. P. Simoncelli, and A. C. Bovik, "Multiscale structural similarity for image quality assessment," in Proc. IEEE Asilomar Conf. Signals Syst. Comput., Pacific Grove, CA, USA, Nov. 2003, pp. 1398-1402.
5. Z. Wang and Q. Li, "Information content weighting for perceptual image quality assessment," IEEE Trans. Image Process., vol. 20, no. 5, pp. 1185-1198, May 2011.
6. A. Liu, W. Lin, and M. Narwaria, "Image quality assessment based on gradient similarity," IEEE Trans. Image Process., vol. 21, no. 4, pp. 1500-1512, Apr. 2012.
7. J. Zhu and N. Wang, "Image quality assessment by visual gradient similarity," IEEE Trans. Image Process., vol. 21, no. 3, pp. 919-933, Mar. 2012.
8. L. Zhang, L. Zhang, X. Mou, and D. Zhang, "FSIM: A feature similarity index for image quality assessment," IEEE Trans. Image Process., vol. 20, no. 8, pp. 2378-2386, Aug. 2011.
9. E. C. Larson and D. M. Chandler, "Most apparent distortion: Fullreference image quality assessment and the role of strategy," J. Electron. Imag., vol. 19, no. 1, 2010, Art. ID 011006.
10. H. R. Sheikh and A. C. Bovik, "Image information and visual quality," IEEE Trans. Image Process., vol. 15, no. 2, pp. 430-444, Feb. 2006.
11. J. Wu, W. Lin, G. Shi, and A. Liu, "Perceptual quality metric with internal generative mechanism," IEEE Trans. Image Process., vol. 22, no. 1, pp. 43-54, Jan. 2013.
12. S. Li, F. Zhang, L. Ma, and K. N. Ngan, "Image quality assessment by separately evaluating detail losses and additive impairments," IEEE Trans. Multimedia, vol. 13, no. 5, pp. 935-949, Oct. 2011.
13. A. K. Moorthy and A. C. Bovik, "Blind image quality assessment: From natural scene statistics to perceptual quality," IEEE Trans. Image Process., vol. 20, no. 12, pp. 3350-3364, Dec. 2011.
14. A. Mittal, A. K. Moorthy, and A. C. Bovik, "No-reference image quality assessment in the spatial domain," IEEE Trans. Image Process., vol. 21, no. 12, pp. 4695-4708, Dec. 2012.
15. Z. Wang and E. P. Simoncelli, "Reduced-reference image quality assessment using a wavelet-domain natural image statistic model," in Proc. SPIE Conf. Human Vis. Electron. Imag., vol. 5666. San Jose, CA, USA, Jan. 2005, pp. 149-159.
16. A. Bouzerdoum, A. Havstad, and A. Beghdadi, "Image quality assessment using a neural network approach," in Proc. IEEE Int. Symp. Signal Process. Inf. Technol, Rome, Italy, 2004, pp. 330-333.
17. P. Carrai, I. Heynderickz, P. Gastaldo, R. Zunino, and P. Monza, "Image quality assessment by using neural networks," in Proc. IEEE Int. Symp. Circuits Syst., vol. 5. Scottsdale, AZ, USA, 2002, pp. V-253-V-256.
18. M. Narwaria, W. Lin, and A. E. Cetin, "Scalable image quality assessment with 2D mel-cepstrum and machine learning approach," Pattern Recognit., vol. 45, no. 1, pp. 299-313, 2012.
19. M. Narwaria and W. Lin, "SVD-based quality metric for image and video using machine learning," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 2, pp. 347-364, Apr. 2012.
20. P. Gastaldo, S. Rovetta, and R. Zunino, "Objective quality assessment of MPEG-2 video streams by using CBP neural networks," IEEE Trans. Neural Netw., vol. 13, no. 4, pp. 939-947, Jul. 2002.
21. P. Le Callet, C. Viard-Gaudin, and D. Barba, "A convolutional neural network approach for objective video quality assessment," IEEE Trans. Neural Netw., vol. 17, no. 5, pp. 1316-1327, Sep. 2006.
22. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, nos. 1-3, pp. 489-501, 2006.
23. T.-J. Liu, W. Lin, and C.-C. J. Kuo, "Image quality assessment using multi-method fusion," IEEE Trans. Image Process., vol. 22, no. 5, pp. 1793-1807, May 2013.
24. D. D. Lee and H. S. Seung, "Learning the parts of objects by nonnegative matrix factorization," Nature, vol. 401, pp. 788-791, Oct. 1999.
25. E. A. Buffalo, P. Fries, R. Landman, H. Liang, and R. Desimone, "A backward progression of attentional effects in the ventral stream," Proc. Nat. Acad. Sci., vol. 107, no. 1, pp. 361-365, 2010.
26. B. C. Motter, "Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli," J. Neurophysiol., vol. 70, no. 3, pp. 909-919, Sep. 1993.
27. G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, "Extreme learning machine for regression and multiclass classification," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 2, pp. 513-529, Apr. 2012.
28. K. Choi, K.-A. Toh, and H. Byun, "Incremental face recognition for large-scale social network services," Pattern Recognit., vol. 45, no. 8, pp. 2868-2883, 2012.
29. C. Pan, D. S. Park, Y. Yang, and H. M. Yoo, "Leukocyte image segmentation by visual attention and extreme learning machine," Neural Comput. Appl., vol. 21, no. 6, pp. 1217-1227, Sep. 2012.
30. R. Minhas, A. A. Mohammed, and Q. M. J. Wu, "Incremental learning in human action recognition based on snippets," IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 11, pp. 1529-1541, Nov. 2012.
31. S. Suresh, R. V. Babu, and H. J. Kim, "No-reference image quality assessment using modified extreme learning machine classifier," Appl. Soft Comput., vol. 9, no. 2, pp. 541-552, 2009.
32. G.-B. Huang, L. Chen, and C.-K. Siew, "Universal approximation using incremental constructive feedforward networks with random hidden nodes," IEEE Trans. Neural Netw., vol. 17, no. 4, pp. 879-892, Jul. 2006.
33. G.-B. Huang and L. Chen, "Convex incremental extreme learning machine," Neurocomputing, vol. 70, pp. 3056-3062, Oct. 2007.
34. G.-B. Huang and L. Chen, "Enhanced random search based incremental extreme learning machine," Neurocomputing, vol. 71, pp. 3460-3468, Oct. 2008.
35. MIT Center for Biological and Computation Learning. (2000). CBCL Face Database #1. [Online]. Available: http://www.ai.mit.edu/projects/cbcl
36. H. Liu, Z. Wu, X. Li, D. Cai, and T. S. Huang, "Constrained nonnegative matrix factorization for image representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 7, pp. 1299-1311, Jul. 2012.
37. W.-S. Zheng, J. Lai, S. Liao, and R. He, "Extracting non-negative basis images using pixel dispersion penalty," Pattern Recognit., vol. 45, no. 8, pp. 2912-2926, 2012.
38. S. Z. Li, X. Hou, H. Zhang, and Q. Cheng, "Learning spatially localized, parts-based representation," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Kauai, HI, USA, 2011, pp. 207-212.
39. W. Xu, X. Liu, and Y. Gong, "Document clustering based on nonnegative matrix factorization," in Proc. Annu. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, Toronto, ON, Canada, 2003, pp. 267-273.
40. F. Shahnaz, M. W. Berry, V. P. Pauca, and R. J. Plemmons, "Document clustering using nonnegative matrix factorization," Inf. Process. Manag., vol. 42, no. 2, pp. 373-386, 2006.
41. N. Ponomarenko. (2008). Tampere Image Database 2008 TID2008. [Online]. Available: http://www.ponomarenko.info/tid2008.htm
42. H. R. Sheikh et al. (2004). MICT Image and Video Quality Assessment Research at Live. [Online]. Available: http://live.ece.utexas.edu/research/quality/
43. E. C. Larson and D. M. Chandler. (2010). Categorical Image Quality (CSIQ) Database. [Online]. Available: http://vision.okstate.edu/csiq
44. P. L. Bartlett, "The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network," IEEE Trans. Inf. Theory, vol. 44, no. 2, pp. 525-536, Mar. 1998.
45. C. R. Rao and S. K. Mitra, Generalized Inverse of Matrices and its Applications, vol. 7. New York, NY, USA: Wiley, 1971.
46. A. E. Hoerl and R. W. Kennard, "Ridge regression: Biased estimation for nonorthogonal problems," Technometrics, vol. 12, no. 1, pp. 55-67, 1970.
47. A. Ninassi, P. L. Callet, and F. Autrusseau, "Pseudo non-reference image quality metric using perceptual data hiding," in Proc. SPIE Conf. Human Vis. Electron. Imag., vol. 6057. San Jose, CA, USA, Jan. 2006, Art. ID 60570G.
48. Y. Horita. (2010). Image Quality Evaluation Database. [Online]. Available: http://mict.eng.u-toyama.ac.jp/database-toyama/
49. D. M. Chandler and S. S. Hemami. (2007). VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images. [Online]. Available: http://foulard.ece.cornell.edu/dmc27/vsnr/vsnr.html
50. Video Quality Expert Group (VQEG). (2003). Final Report From the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment II. [Online]. Available: http://www.vqeg.org/
51. P. Bartlett, S. Boucheron, and G. Lugosi, "Model selection and error estimation," J. Mach. Learn., vol. 48, nos. 1-3, pp. 85-113, Jul. 2002.