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For the purpose of algorithm comparison I want to compare the detection result of my cascade classifier trained using the viola & jones framework versus an SVM HOG classifier I have trained.

However, the VJ framework has no problem with an uneven ratio for positives and negatives, for example 500 positives and 100 negatives (don't tell me this is wrong please, my whole PhD is about proving that context awareness can reduce the negative numbers effectively. .

If I would like to convert this to SVM classification however, I am reading at many blogposts that using CvSVM should be done with an equal number of positive and negative training samples. If not I should add a bias, but however, I cannot find an example that does exactly that.

Could anyone give me some directions on how I should apply this? Is this even possible within the OpenCV framework?

Kind regards,

Steven

For the purpose of algorithm comparison I want to compare the detection result of my cascade classifier trained using the viola & jones framework versus an SVM HOG classifier I have trained.

However, the VJ framework has no problem with an uneven ratio for positives and negatives, for example 500 positives and 100 negatives (don't tell me this is wrong please, my whole PhD is about proving that context awareness can reduce the negative numbers effectively. .

If I would like to convert this to SVM classification however, I am reading at many blogposts that using CvSVM should be done with an equal number of positive and negative training samples. If not I should add a bias, but however, I cannot find an example that does exactly that.

Could anyone give me some directions on how I should apply this? Is this even possible within the OpenCV framework?

Kind regards,

Steven

For the purpose of algorithm comparison I want to compare the detection result of my cascade classifier trained using the viola & jones framework versus an SVM HOG classifier I have trained.

However, the VJ framework has no problem with an uneven ratio for positives and negatives, for example 500 positives and 100 negatives (don't tell me this is wrong please, my whole PhD is about proving that context awareness can reduce the negative numbers effectively. .

If I would like to convert this to SVM classification however, I am reading at many blogposts that using CvSVM should be done with an equal number of positive and negative training samples. If not I should add a bias, but however, I cannot find an example that does exactly that.

Could anyone give me some directions on how I should apply this? Is this even possible within the OpenCV framework?

Kind regards,

Steven

EDIT 1: I have noticed people suggest using libSVM or SVMlight for training the model. However, I would like to stick to openCV only, which should actually be possible. Anyone?possible.

For the purpose of algorithm comparison I want to compare the detection result of my cascade classifier trained using the viola & jones framework versus an SVM HOG classifier I have trained.

However, the VJ framework has no problem with an uneven ratio for positives and negatives, for example 500 positives and 100 negatives (don't tell me this is wrong please, my whole PhD is about proving that context awareness can reduce the negative numbers effectively. .

If I would like to convert this to SVM classification however, I am reading at many blogposts that using CvSVM should be done with an equal number of positive and negative training samples. If not I should add a bias, but however, I cannot find an example that does exactly that.

Could anyone give me some directions on how I should apply this? Is this even possible within the OpenCV framework?

Kind regards,

Steven

EDIT 1: I have noticed people suggest using libSVM or SVMlight for training the model. However, I would like to stick to openCV only, which should actually be possible.

For the purpose of algorithm comparison I want to compare the detection result of my cascade classifier trained using the viola & jones framework versus an SVM HOG classifier I have trained.

However, the VJ framework has no problem with an uneven ratio for positives and negatives, for example 500 positives and 100 negatives (don't tell me this is wrong please, my whole PhD is about proving that context awareness can reduce the negative numbers effectively. .

If I would like to convert this to SVM classification however, I am reading at many blogposts that using CvSVM should be done with an equal number of positive and negative training samples. If not I should add a bias, but however, I cannot find an example that does exactly that.

Could anyone give me some directions on how I should apply this? Is this even possible within the OpenCV framework?

Kind regards,

Steven

EDIT 1: I have noticed people suggest using libSVM or SVMlight for training the model. However, I would like to stick to openCV only, which should actually be possible.