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This is what i did, but don't know why it have this problem ..

This is my parameter for the train_auto :

 CvParamGrid CvParamGrid_C(pow(2.0, -5), pow(2.0, 15), pow(2.0, 2));

CvParamGrid CvParamGrid_gamma(pow(2.0, -15), pow(2.0, 3), pow(2.0, 2));

CvSVMParams param;

param.kernel_type = CvSVM::RBF;

param.svm_type = CvSVM::C_SVC;

param.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 0.000001);

    classes_classifiers[class_].train_auto(samples_32f, labels, Mat(), Mat(),

        param, 10, CvParamGrid_C, CvParamGrid_gamma,

        CvSVM::get_default_grid(CvSVM::P),

        CvSVM::get_default_grid(CvSVM::NU),

        CvSVM::get_default_grid(CvSVM::COEF),

        CvSVM::get_default_grid(CvSVM::DEGREE), true);

So i try the train auto to have the best parameter, but i got another issue .. When i am testing my program with two class, it works fine, for example, i train the svm with banana and apple, and whent i test with a picture of a banana, it works, but when i add a class to my training data, and use the one vs all strategy, it can't detect that it is a banana ..

60 images per class ^^ But ya i think this is not enough But about the parameter of the SVM, i read that cross validation is a good way to find the best param, but what are the interval for the parameter ? Because, if i have to test different parameter, i should have a interval i think .. Does opencv train_auto function use the best parameter ?

My params : params.kernel_type = CvSVM::RBF; params.svm_type = CvSVM::C_SVC; params.gamma = 0.50625000000000009; params.C = 312.50000000000000;

data : 60 images

class : 4

Hi again :) So, it is not color histogram problem ? If you jcan ust tell me if i am right please, I divide the image into grids, in each grids, i use calcHist() to extract red, greenblue histogram, i made the average to construct my histogram ==> hit (1) = histRed.at(1) + histGreen.at(1) + histRed.at(1) I repeat this until i've done all the grids, and after that, i use the matrix for the svm

Hello,

I would like to recognise fruit using color histogram, and svm. But whatever i am doing, the result of the SVM is false. Does someone can give me link to use color histogram for object recognition please or just give some clues?

Actually, i am dividing an image into 12*12 blocks, and then extract rgb color histogram from it. I use this result for my SVM.

When i test the program, all the result seems bad, i think i am doing something wrong.

Oww .. oki thank you very much for your help, i will try another algorithm :)

Yes my bad sorry :) , this is the link http://img.cs.uec.ac.jp/pub/conf12/12...

This is my problem, the paper on which i am working is adopting bag-of-features with SURF and color histogram as image features, don't know how to deal with it, to create the bag of features, i used surf, and colors histogram, and concat their vectors. But, when i am doing the SVM part, for the image classification, i don't know how to use my Bof ..

So, first of all, i construct Bof with Surf and color histogram. After that, i used surf to detect keypoints, and use this keypoint for bof compute function, and concat this result with color histogram ? But, if the bof already contains color information, i thought it coul be another way .. or i am wrong, i am newbie so this a little complicated for me ^^

Hi everyone,

I am working on a project and i hope someone could help me. I need to combine Surf and color histogram to use as features for my SVM. Actually, i have to use bag-of-features with SURF and color histogram as image features and linear SVM with the one-vs-rest strategy as a classifier. I can construct Bof by combining the two vectors, but when comes the part of the SVM, i don't know what to do, because, i have to use compute function from the bof, but i think i would not have color information.