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Hi all,,

I'm using a binary SVM to classify positive and negative sets of images encoded using HOG descriptors to the machine; I labeled my positive set '1' and '0' for the negative one during training. /expereienced

Based on the OpenCV manual, using float response = predict(inputMat, true); returns the signed decision function value. Does the negative sign denotes a Positive label '1'? I made few experiments* to reach this conclusion and I just want a confirmation from anyone knowledgeable/experienced with SVMs.

*The experiments was applied on 2 positives and 2 negatives using float response = predict(inputMat, true); once and then using float response = predict(inputMat); //Default false, returns label. I hope my conclusion applies to any image. Thank you :)

Hi all,,

I'm using a binary SVM to classify positive and negative sets of images encoded using HOG descriptors to the machine; I labeled my positive set '1' and '0' for the negative one during training. /expereienced

Based on the OpenCV manual, using float response = predict(inputMat, true); returns the signed decision function value. Does the negative sign denotes a Positive label '1'? I made few experiments* to reach this conclusion and I just want a confirmation from anyone knowledgeable/experienced with SVMs.

*The experiments was applied on 2 positives and 2 negatives using float response = predict(inputMat, true); once and then using float response = predict(inputMat); //Default false, returns label. I hope my conclusion applies to any image. Thank you :)

Hi all,,

I'm using a binary SVM to classify positive and negative sets of images encoded using HOG descriptors to the machine; I labeled my positive set '1' and '0' for the negative one during training.

Based on the OpenCV manual, using float response = predict(inputMat, true); returns the signed decision function value. Does the negative sign denotes a Positive label '1'? I made few experiments* to reach this conclusion and I just want a confirmation from anyone knowledgeable/experienced with SVMs.

*The experiments was applied on 2 positives and 2 negatives using float response = predict(inputMat, true); once and then using float response = predict(inputMat); //Default false, returns label. I hope my conclusion applies to any image. Thank you :)

Hi all,,

I'm using a binary SVM to classify positive and negative sets of images encoded using HOG descriptors to the machine; I labeled my positive set '1' and '0' for the negative one during training.

Based on the OpenCV manual, using float response = predict(inputMat, true); returns the signed decision function value. Does the negative sign denotes a Positive label '1'? I made few experiments* to reach this conclusion and I just want a confirmation from anyone knowledgeable/experienced with SVMs.

*The experiments was applied on 2 positives and 2 negatives using float response = predict(inputMat, true); once and then using float response = predict(inputMat); //Default false, returns label. I hope my conclusion applies to any image. Thank you :)

Hi all,,

I'm using a binary SVM to classify positive and negative sets of images encoded using HOG descriptors to the machine; I labeled my positive set '1' and '0' for the negative one during training.

Based on the OpenCV manual, using float response = predict(inputMat, true); returns the signed decision function value. Does the negative sign denotes a Positive label '1'? I made few experiments* to reach this conclusion and I just want a confirmation from anyone knowledgeable/experienced with SVMs.

*The experiments was applied on 2 positives and 2 negatives using float response = predict(inputMat, true); once and then using float response = predict(inputMat); //Default false, returns label. I hope my conclusion applies to any image. Thank you :)