I'm currently collating images to try and develop a HAAR classifier using the OpenCV traincascade functionality. The objects I want to detect are small and numerous (bees), and I'm curious as to whether it would be better to train the image with lots of small cutouts of the bees as positive images, or to use bigger positive images with the bees present along with a chunk of the background.
You probably want to train on as tightly cropped rectangular region around the bee as possible.
I'm not sure if you already thought of this but you should remember that this classifier can only handle recognizing objects in rectangles whose axes are aligned with image, i.e. no rotated rectangles. This is going to be tricky if the bees can be facing any which way. A human face detector has the advantage that faces are almost always found right side up in the image. If you wanted to recognize upside down faces too you would probably train only on right side up images and then check your input image twice--once with the original input and once with it flipped upside down.
You probably want to do something similar for the bees. All of your training images should be aligned so that the bee has approximately same orientation and scale. Then if you want to detect different orientations you can rotate your input image.