图像识别基于ksvd字典学习之人脸表情识别matlab源码

Posted 2021-05-24 Matlab走起

 

1. %
   %
   % Main Program of KSVD-NN based facial expression recognition.
   
   clear
   datadim='37x30';
   %datadim='50x40';
   %datadim='gabor_all';
   testmethod='unfamiliar';
    
    
   % prepare image data
   [data,label] = PrepareData(datadim,testmethod,9);
    
   % direct nearest neighbor classification
   testresult = nearestNeighbor( data.train , label.train , data.test );
   rate = length( find( ( testresult - label.test ) == 0 ) ) / length(label.test);
   clear testresult;
   fprintf('\\n Direct nearest neighbot on pixel values: rec rate: %f \\n',rate);
    
    
   % training process using KSVD
   param.L = 12;
   param.K = 90;
   param.numIteration = 20;
   param.errorFlag = 0;
   param.preserveDCAtom = 0;
   param.InitializationMethod = 'DataElements';
   param.displayProgress = 1;
   disp('Starting to  train the dictionary');
   tt=cputime;
   [Dictionary,KSVDout]  = KSVD(data.train,param);
   fprintf('\\ntime of K-SVD: %f\\n\\n' , cputime - tt);
   %I=showdict(Dictionary,[37,30],10,8,'lines') ;
   %imshow(I);
   %KSVDout.CoefMatrix = full( KSVDout.CoefMatrix );
   tt=cputime;
   % Using OMP to find the sparse coefficients for test samples
   coeftest = OMP(Dictionary,data.test,param.L);
   %coeftest = full( coeftest );
    
   % nearest neighbor classification
   testresult = nearestNeighbor( KSVDout.CoefMatrix , label.train , coeftest );
   fprintf('\\ntime of testing: %f\\n\\n' , cputime - tt);
   rate = length( find( ( testresult - label.test ) == 0 ) ) / length(label.test);
    
   fprintf('\\n The result when image dimension: %s    test-method: %s \\n' , datadim, testmethod );
   fprintf(' L(sparsity of coef) = %d, K(number of atoms) = %d : recognition rate: %f \\n\\n\\n',param.L , param.K , rate);
    
   % direct pixal values
   testresult = nearestNeighbor( data.train , label.train , data.test );
   rate = length( find( ( testresult - label.test ) == 0 ) ) / length(label.test);

   

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