Digit Recognition Based on Distance Metric by Gabor Transformation

Abstract:

Distance metric is important for image transform, it reflects the similarity of manifold between pre and post transformation. Tangent distance method is usually used to approximate nonlinear manifolds by its tangent hyperplane in digit recognition; however, it sometimes neglects the high-order statistic information of the image. A new image distance metric is proposed in this paper which is based on Gabor transformation to obtain the Gabor feature vector, then the feature manifold can be approximated by curve surfaces based on second-order Taylor expansion with respect to intrinsic variables, and the distance metric by Gabor transformation is defined as the minimum distance between the approximated curved surfaces in feature observation space. Experiments show that the distance metric based on Gabor transformation and manifold works well on digit recognition, it excelled tangent distance method.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 1548-1551
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.1548
• Online since: September 2013
• Authors: Shi Dong Li*, Qing Ai, Song Liu, Hua Zhou
• Keywords: Digit Recognition, Distance Metric, Gabor Transformation

References

[1] L. Wang, Y. Zhang and J. Feng: On the Euclidean distance of images, IEEE transactions on pattern analysis and machine intelligence, Vol. 27 (2005), pp.1334-1339.

DOI: 10.1109/tpami.2005.165

[2] I. H. Witten, A. Moffat and T. C. Bell: Managing gigabytes: compressing and indexing documents and images (Morgan Kaufmann, USA 1999).

DOI: 10.1109/tit.1995.476344

[3] R. Fooprateepsiri and W. Kurutach: A Highly Robust Approach Image Identification based-on Hausdorff-Trace Transform, International Journal of Digital Content Technology and its Applications, Vol. 4 (2010), pp.26-31.

DOI: 10.4156/jdcta.vol4.issue1.3

[4] H. S. Seung and D. D. Lee: Cognition. The manifold ways of perception, Science, Vol. 290 (2000), pp.2268-2269.

DOI: 10.1126/science.290.5500.2268

[5] J. J. DiCarlo and D. D. Cox: Untangling invariant object recognition, Trends Cogn. Sci., Vol. 11 (2007), pp.333-341.

DOI: 10.1016/j.tics.2007.06.010

[6] R. Pless and R. Souvenir: A survey of manifold learning for images, IPSJ Transactions on Computer Vision and Applications, Vol. 1 (2009), pp.83-94.

DOI: 10.2197/ipsjtcva.1.83

[7] J. Zhang, H. Huang and J. Wang: Manifold Learning for Visualizing and Analyzing High-Dimensional Data, IEEE Intelligent Systems, Vol. 25 (2010), pp.54-61.

DOI: 10.1109/mis.2010.8

[8] S. Lafon and A. B. Lee: Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization, IEEE transactions on pattern analysis and machine intelligence, Vol. 28 (2006).

DOI: 10.1109/tpami.2006.184

[9] G. E. Hinton and R. R. Salakhutdinov: Reducing the dimensionality of data with neural networks, Science, Vol. 313 (2006), pp.504-507.

DOI: 10.1126/science.1127647

[10] K. Weinberger and L. Saul: Unsupervised Learning of Image Manifolds by Semidefinite Programming, International Journal of Computer Vision, Vol. 70 (2006), pp.77-90.

DOI: 10.1007/s11263-005-4939-z

[11] B. Borchers: CSDP, a C library for semidefinite programming, Optimization Methods and Software, Vol. 11 (1999), pp.613-623.

DOI: 10.1080/10556789908805765

[12] E. Kussul and T. Baidyk: Improved method of handwritten digit recognition tested on MNIST database, Image and Vision Computing, Vol. 22 (2004), pp.971-981.

DOI: 10.1016/j.imavis.2004.03.008