Im trying implement a real time object classification program using SVM classification and BoW clustering algorithms. My questions is what are the good practices for selecting positive and negative training images?
Positive image sets
- Should the background be empty? Meaning, should the image only contain the object of interest? When implementing this algorithm in real time, the test image will not contain only the object of interest, it will definitely have some information from the background as well. So instead of using isolated image collection, should I choose images which look more similar to the test images?
Negative image sets
- Can these be any image set without the object of interest? Or should they be from the environment where this algorithm is going to be tested without object of interest?. For example, if I'm going to classify phones in my living room environment, should negatives be the background image set of my living room environment without the phone in the foreground? or can it be any image set? (like kitchen, living room, bedroom or outdoor images) Im asking this because, I don't want the system to be environment-specific. Must be robust at any environment (indoors and outdoors)
Thank you. Any help or advice is much appreciated.
