Feature Extraction and Classification of Electric Power Equipment Images Based on Corner Invariant Moments

Feature extraction and accurate classification of electric power equipment, help to improve the automation and intelligent level of power system management. Aiming at the problems that applying Hu invariant moments to extract image feature computes large and applying corner vector to match has too dimensions, this paper presented Harris corner invariant moments algorithm. This algorithm only calculates corner coordinates other than the entire image coordinates, so can change the point feature into feature vectors, and reduce the corner matching dimensions. Combined with the SVM (Support Vector Machine) classification method, we conducted a classification for a large number of electrical equipment images, and the result shows that using Harris corner invariant moments algorithm to extract invariant moments, and classifying by these invariant moments can achieve better classification accuracy.