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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7-8/2014

01.12.2014 | Original Article

Image classification using local linear regression

verfasst von: Wankou Yang, Karl Ricanek, Fumin Shen

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2014

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Abstract

In the past several decades, classifier design has attracted much attention. Inspired by the locality preserving idea of manifold learning, here we give a local linear regression (LLR) classifier. The proposed classifier consists of three steps: first, search k nearest neighbors of a pointed sample from each special class, respectively; second, reconstruct the pointed sample using the k nearest neighbors from each special class, respectively; and third, classify the test sample according to the minimum reconstruction error. The experimental results on the ETH80 database, the CENPAMI handwritten number database and the FERET face image database demonstrate that LLR works well, leading to promising image classification performance.
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Literatur
1.
Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268MATHMathSciNet
2.
Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27CrossRefMATH
3.
Yu K, Ji L, Zhang X (2002) Kernel nearest-neighbor algorithm. Neural Process Lett 15:147–156CrossRefMATH
4.
Li SZ, Lu J (1999) Face recognition using the nearest feature line method. IEEE Trans Neural Netw 10(2):439–443CrossRef
5.
Zheng W, Zhao L, Zou C (2004) Locally nearest neighbour classifiers for pattern recognition. Pattern Recognit 37(6):1307–1309CrossRefMATH
6.
Lou Z, Jin Z (2006) Novel adaptive nearest neighbour classifiers based on hit-distance. In: Proceedings of the 18th international conference on pattern recognition (ICPR2006), vol. 3, pp. 87–90
7.
Gao Q, Wang Z (2007) Center-based nearest neighbor classifier. Pattern Recognit 40(1):346–349CrossRefMATH
8.
Shen F, Hasegawa O (2008) A fast nearest neighbor classifier based on self-organizing incremental neural network. Neural Netw 21(10):1537–1547CrossRefMATH
9.
Mitani Y, Hamamoto Y (2006) A local mean-based nonparametric classifier. Pattern Recognit Lett 27(10):1151–1159CrossRef
10.
Zhang C, Zhang J (2008) RotBoost: a technique for combining Rotation Forest and AdaBoost. Pattern Recognit Lett 29(10):1524–1536CrossRef
11.
Zhou A, Zhu Z, Fan H (2012) A new semi-supervised PSVM classifier. Appl Math Comput 219(8):883–889MathSciNet
12.
Ben X, Meng W, Yan R, Wang K (2013) Kernel coupled distance metric learning for gait recognition and face recognition. Neurocomputing 2013(120):577–589CrossRef
13.
Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw patches. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR 2008)
14.
Rao S, Tron R, Vidal R, Ma Y (2008) Motion segmentation via robust subspace separation in the presence of outlying, incomplete, and corrupted trajectories. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR 2008)
15.
Mairal J, Sapiro G, Elad M (2008) Learning multiscale sparse representations for image and video restoration. SIAM MMS 7(1):214–241CrossRefMATHMathSciNet
16.
Wright J, Yang A, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRef
17.
Wright J, Ma Y, Mairial J, Sapiro G, Huang T, Yan S (2010) Sparse representation for computer vision and pattern recognition. In Proceedings of IEEE, special issue on applications of compressive sensing & sparse representation, 98(6): 1031–1044
18.
Rigamonti R, Brown MA, Lepetit V (2011) Are sparse representations really relevant for image classification? In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR2011)
19.
Shi Q, Erisson A, van den Hengel A, Shen C (2011) Is face recognition really a compressive sensing problem? In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR 2011)
20.
Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition. In Proceedings of IEEE conference on computer vision (ICCV2011)
21.
Yang W, Wang Z, Yin J, Sun C, Ricanek K (2013) Image classification using Kernel collaborative representation with regularized least square. Appl Math Comput 222(13–28):2013MathSciNet
22.
Xu Y, Zhang D, Yang J, Yang J-Y (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 21(9):1255–1262CrossRef
23.
Qiao L, Chen S, Tan X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recognit 43(1):331–341CrossRefMATH
24.
Lai Z, Xu Y, Yang J, Zhang D (2013) Sparse tensor discriminant analysis. IEEE Trans Image Process 22(10):3904–3915CrossRefMathSciNet
25.
Lai Z, Wong WK, Jin Z, Yang J, Xu Y (2012) Sparse approximation to the eigensubspace for discrimination. IEEE Trans Neural Netw Learn Syst 23(12):1948–1960CrossRef
26.
Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112CrossRef
27.
Chai X, Shan S, Chen X, Gao W (2007) Locally linear regression for pose invariant face recognition. IEEE Trans Image Process 16(7):1716–1725CrossRefMathSciNet
28.
Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning; data mining, inference and prediction. Springer, New YorkMATH
29.
30.
Leibe B, Schiele B (2003) Analyzing appearance and contour based methods for object categorization. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR 2003)
31.
32.
Yang J, Zhang L, Yang J, Zhang D (2011) From classifiers to discriminators: a nearest neighbor rule induced discriminant analysis. Pattern Recognit 44(7):1387–1402CrossRefMATH
33.
Liao SX, Pawlark M (1996) On image analysis by moments. IEEE Trans Pattern Anal Mach Intell 18(3):254–266CrossRef
34.
Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104CrossRef
35.
Metadaten
Titel
Image classification using local linear regression
verfasst von
Wankou Yang
Karl Ricanek
Fumin Shen
Publikationsdatum
01.12.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7-8/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1681-2

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