フィルターのクリア

Info

この質問は閉じられています。 編集または回答するには再度開いてください。

error occured during fusion with GLCM and invarient moment feature

1 回表示 (過去 30 日間)
Balaji M. Sontakke
Balaji M. Sontakke 2020 年 2 月 25 日
閉鎖済み: MATLAB Answer Bot 2021 年 8 月 20 日
Here i combined(fused) two different type of features i.e invarient moment and GLCM. When i classify with individual invarient moment or GLCM i got 98.75 % accuracy but when i combine these two features with following program i got 25% accuracy, what is the problem in my program i dont understand.
clear all;
clc;
tic; %% calculating elapsed time for execution
%% load mat files
test = {load('db3.mat'),load('db5.mat')};
train = {load('db4.mat'),load('db6.mat')};
n1 = cellfun(@fieldnames,test,'un',0);
n2 = cellfun(@fieldnames,train,'un',0);
V1 = cellfun(@(x,y)[x.(y)],test,[n1{:}],'un',0);
V2 = cellfun(@(x,y)[x.(y)],train,[n2{:}],'un',0);
P_test = cell2mat(reshape(cat(1,V1{:}),100,[])); %for 100 classes
P_train = cell2mat(reshape(cat(1,V2{:}),200,[])); %for 100 classes
%% labeling class
train_label=load('train_label_100.txt');
test_label=load('test_label_100.txt');
%% Normalisation by Z - Scores
P_train = zscore(P_train,0,2);
P_test =zscore(P_test,0,2);
%% classfication
predictlabel = knnclassify(P_test, P_train, train_label,2,'cosine','nearest');
cp = classperf(test_label,predictlabel);
Conf_Mat = confusionmat(test_label,predictlabel);
disp(Conf_Mat);
%% % Evaluate Performance
[FPR, TPR,Thr, AUC, OPTROCPT] = perfcurve(predictlabel, test_label,1);
figure,
plot(TPR,FPR,'r-','LineWidth',1);
xlabel('False positive rate')
ylabel('True positive rate')
title('ROC Curve for Classification ')
t = table(FPR, TPR, Thr);
fprintf('\n\n Overall accuracy:%f%%\n',cp.CorrectRate*100);
%% calculating elapsed time for execution
toc

回答 (0 件)

この質問は閉じられています。

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by