you can train a classification SVM on multiple classes
(it will dispatch to multiple “one-against-all-others” binary SVM’s internally),
however, for detection , a single support vector is trained,
and the dot product of that, and the hog-feature of a sliding window in the image is used to determine if it’s an object or not
I have read the HOGDescripotor object detection tutorial (that used SVM). THis tutorial is all the knowlege I have about SVM. I found the source code, the way it was structured difficult to follow, so some parts are difficult to remember/understand.
So I ask; In the tutorial, 2 objects types were given to the SVM to be trained, each with an index/description/label, This was an example of Positive vs Negative. How would I use SVM as a multiclass classifier?, does it allow more than 2 objects and index/description/labels ?.
however, for detection , a single support vector is trained,
(from the HOG Descriptor tutorial) , is this due to the Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector, can only support trained models that are binary/for single obkects per model ?. Is this where the limitation is?.