Advice to consider when training a robust cascade classifier?

Question

I\'m training a cascade classifier in order to detect animals in images. Unfortunately my false positive rate is quite high (super high using Haar and LBP, acceptable using HOG)

Answer 1

• 4000 pos - 800 neg is a bad ratio. The thing with negative samples is that you need to train your system as many of them as possible, since Adaboost ML algorithm -the core algorithm for all haar like feature selection processes- depends highly on them. Using 4000 / 10000 would be a good enhancement.
• Detecting "animals" is a hard problem. Since your problem is a decision process, which is already NP-hard, you are increasing complexity with your range of classification. Start with cats first. Have a system that detects cats. Then apply the same to the dogs. Have, say 40 systems, detecting different animals and use them for your purpose later on.
• For training, do not use occluded objects as positives. i.e. if you want to detect frontal faces, then train frontal faces with only applying position and orientation changes, without including any other object in front of it.
• Downscaling is not important as the haar classifier itself downscales everything to 24x24. Watch whole viola-jones presentation when you have enough time.
• Good luck.