Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm

Abstract This paper use the fractional differential mask operator to describe and deal with the highly self similar digital medical image, then extracting the main features of the these images with principal component analysis, and recognizing these images with SVM algorithm. To provide an efficient algorithm for medical image processing with high degree of self similarity. Based on the analysis of fractional differential theory, principal component analysis theory and support vector machine (SVM) algorithm, designing a Fractional-order-PCA-SVM coupling algorithm for digital image recognition. and taking the digital image in ORL face database as the empirical object, using PCA-NN coupling algorithm, SVM algorithm, PCA-SVM algorithm, Sobel-PCA-NN algorithm, coupling coupling Sobel-SVM coupling algorithm, Sobel-PCA-SVM algorithm, Fractional-order-PCA-NN algorithm and coupling coupling Fractional-order-SVM coupling algorithm as the comparison algorithm, creating four experiments with the same sample, regarding the speed of operation and the accuracy of recognition as the criteria of high evaluation, verifying the superiority of the Fractional-order-PCA-SVM coupling algorithm designed in this paper. The average run time of the Fractional-order-PCA-SVM coupling algorithm only 4.152 s in the four experiments, although the speed of operation is not the highest, it also meets the demand of identification. The reason that the operation speed of the algorithm slightly lower than the three control algorithms is that the three algorithms are monotonic; The average accuracy rate of Fractional-order-PCA-SVM coupling algorithm is 99.2425% in four experiments, significantly better than the eight comparison algorithm; For digital medical image recognition the coupling algorithm designed in this paper is effective.

[1]  Song Lina.  Analysis of factors affecting investment efficiency based on analytic hierarchy process and support vector machine (SVM) model , 2018, Cluster Computing.

[2]  N. Alaa,et al.  The rainfall-runoff model GR4J optimization of parameter by genetic algorithms and Gauss-Newton method: application for the watershed Ourika (High Atlas, Morocco) , 2017, Arabian Journal of Geosciences.

[3]  Anil K. Jain,et al.  Learning Face Image Quality From Human Assessments , 2018, IEEE Transactions on Information Forensics and Security.

[4]  Sun Bin-xuan.  A New Method of Face Recognition Base on Support Vector Machine , 2011 .

[5]  Jing-Wein Wang,et al.  Illumination compensation for face recognition using adaptive singular value decomposition in the wavelet domain , 2018, Inf. Sci..

[6]  Wei Zhang,et al.  Infinite Bayesian one-class support vector machine based on Dirichlet process mixture clustering , 2018, Pattern Recognit..

[7]  Tingting Wang,et al.  Research on Workshop-Based Positioning Technology Based on Internet of Things in Big Data Background , 2018, Complex..

[8]  Yang Han,et al.  Situational Awareness System in the Smart Campus , 2018, IEEE Access.

[9]  Xin-Ping Guan,et al.  A novel face recognition method based on IWLD and IWBC , 2015, Multimedia Tools and Applications.

[10]  Mohammed Azmi Al-Betar,et al.  Optimized symmetric partial facegraphs for face recognition in adverse conditions , 2018, Inf. Sci..

[11]  Seth D. Guikema,et al.  Natural disaster risk analysis for critical infrastructure systems: An approach based on statistical learning theory , 2009, Reliab. Eng. Syst. Saf..

[12]  Fang Yu,et al.  Internet of things for smart ports: Technologies and challenges , 2018, IEEE Instrumentation & Measurement Magazine.

[13]  Wei Gao,et al.  Distance learning techniques for ontology similarity measuring and ontology mapping , 2017, Cluster Computing.

[14]  Wei Gao,et al.  The fifth geometric-arithmetic index of bridge graph and carbon nanocones , 2017 .

[15]  Shaju K. Albert,et al.  Signal processing approach on weld data for evaluation of arc welding electrodes using probability density distributions , 2019, Measurement.

[16]  Michael Beitelschmidt,et al.  Edge detection based on fractional order differentiation and its application to railway track images , 2015 .

[17]  Matti Schneider,et al.  Fiber orientation interpolation for the multiscale analysis of short fiber reinforced composite parts , 2018 .

[18]  Seth D. Guikema,et al.  Risk analysis for critical infrastructure systems: An approach based on statistical learning theory , 2008 .

[19]  Yu Zhou,et al.  The artificial neural network prediction algorithm research of rail-gun current and armature speed based on B-dot probes array , 2019 .

[20]  Andrew W. Fitzgibbon,et al.  A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning , 2018, Optim. Methods Softw..

[21]  Kimiaki Shirahama,et al.  Towards large-scale multimedia retrieval enriched by knowledge about human interpretation , 2014, Multimedia Tools and Applications.