Medical image classification algorithm based on principal component feature dimensionality reduction

被引：3
|
作者：
Kong MingRu [1 ,2 ]
Qin Zheng [3 ]
Yan, Song Kui [1 ]
Arunkumar, N. [4 ]
机构：
[1] Northeast Forestry Univ, Mat Sci & Engn Coll, Harbin 150040, Heilongjiang, Peoples R China
[2] Mudanjiang Med Coll, Hongqi Hosp, Informat Ctr, Mudan River 157011, Peoples R China
[3] Northeast Forestry Univ, Coll Informat & Comp Engn, Harbin 150040, Heilongjiang, Peoples R China
[4] Solamalai Coll Engn, Madurai, Tamil Nadu, India
来源：
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2019年 / 98卷
关键词：
Similarity measure of images; Target detection; Image forgery; Support vector machine (SVM); Feature matching; PARTICLE-SWARM OPTIMIZATION; COMPENSATION;
D O I：
10.1016/j.future.2018.11.056
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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. (C) 2019 Elsevier B.V. All rights reserved.
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页码：627 / 634
页数：8
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