Dimensionality Reduction of Colored Images Using 2DPCA

An approach to dimensionality reduction using 2DPCA is presented for colored images. This research also investigates the mechanism of reducing computational complexity for computer vision applications. Since high dimensional data poses various problems like increased computational complexity and increased processing time, there is a dire need of incorporating dimensionality reduction as a preprocessing step in various applications to reduce these factors, which becomes worst for color image processing.

[1]  Mei Han,et al.  Fast Covariance Computation and Dimensionality Reduction for Sub-window Features in Images , 2010, ECCV.

[2]  Andrew W. Fitzgibbon,et al.  Interactive Feature Tracking using K-D Trees and Dynamic Programming , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[4]  Mohammed Bennamoun,et al.  1D-PCA, 2D-PCA to nD-PCA , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[5]  Thomas Deselaers,et al.  ClassCut for Unsupervised Class Segmentation , 2010, ECCV.

[6]  Alexandra Psarrou,et al.  Key-frame extraction algorithm using entropy difference , 2004, MIR '04.

[7]  Daoqiang Zhang,et al.  (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition , 2005, Neurocomputing.

[8]  H. M. Ebied.  Kernel-PCA for face recognition in different color spaces , 2012, 2012 Seventh International Conference on Computer Engineering & Systems (ICCES).

[9]  H. M. Ebied.  Feature extraction using PCA and Kernel-PCA for face recognition , 2012, 2012 8th International Conference on Informatics and Systems (INFOS).