New learning subspace method for image feature extraction

Abstract

A new method of Windows Minimum/Maximum Module Learning Subspace Algorithm (WMMLSA) for image feature extraction is presented. The WMMLSM is insensitive to the order of the training samples and can regulate effectively the radical vectors of an image feature subspace through selecting the study samples for subspace iterative learning algorithm, so it can improve the robustness and generalization capacity of a pattern subspace and enhance the recognition rate of a classifier. At the same time, a pattern subspace is built by the PCA method. The classifier based on WMMLSM is successfully applied to recognize the pressed characters on the gray-scale images. The results indicate that the correct recognition rate on WMMLSM is higher than that on Average Learning Subspace Method, and that the training speed and the classification speed are both improved. The new method is more applicable and efficient.

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