Local Subspace Classifier with Gabor Filter Decomposition for Image Classification

This paper presents a local subspace classifier (LSC) with Gabor filter decomposition for image classification. In our method, first, the training images are decomposed into different directions by Gabor filters. By the same way as training images, an input image is decomposed into different directions with Gabor filters. After this, LSC is applied to each direction domain independently. The total sum of distances calculated from each direction is used for final classification. This method may be simple but we can improve accuracy by it. Experimental results on modified USPS, CIFAR-10, and SVHN datasets show that Gabor decomposition is effective for improving image classification accuracy of LSC.

[1]  Andrew Y. Ng,et al.  Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .

[2]  Kazuhiro Hotta A robust face detector under partial occlusion , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[3]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[4]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Jorma Laaksonen Local Subspace Classifier , 1997, ICANN.

[6]  J. Daugman Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[7]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[8]  Yann LeCun,et al.  Regularization of Neural Networks using DropConnect , 2013, ICML.

[9]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[10]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[11]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[12]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[13]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[14]  David G. Stork,et al.  Pattern Classification , 1973 .