Medical image classification algorithm based on principal component feature dimensionality reduction

Abstract

Abstract A detection technique of digital image forgery based on local descriptor of multi-resolution Weber was proposed on the basis of Weber’s Law pertinent to deficiencies such as low accuracy, weak adaptability and simplicity of current detection algorithm of digital image forgery. WLD feature was extracted from chrominance channel of images, and more characteristic quantities could be extracted compared with single resolution through introduction of multi-resolution; meanwhile, WLD histogram could be formed under different resolutions in directions of differential excitation and gradient through optimization of WLD parameters; then classification could be conducted with SVM. Experimental data indicates: WLD of multi-resolution has better detection result compared with single resolution, and WLD of multi-resolution has better detection performance in detection of splicing forgery images and copying–moving forgery images. Forgery detection experiment in many image data bases indicates: WLD of multi-resolution has better detection result compared with single resolution, and WLD of multi-resolution has better detection performance in detection of splicing forgery images and copying–moving forgery images.