Haar-like features

Haar-like features are so called because they share an intuitive similarity with the Haar wavelets.

Historically, for the task of object recognition, working with only image intensities ( i.e. the RGB pixel values at each and every pixel of image) made the task computationally expensive. A publication by Papageorgiou et al. discussed working with an alternate feature set instead of the usual image intensities. This feature set considers rectangular regions of the image and sums up the pixels in this region. The value this obtained is used to categorize images. For example, let us say we have an image database with human faces and buildings. It is possible that if the eye and the hair region of the faces is considered, the sum of the pixels in this region would be quite high for the human faces and arbitrarily high or low for the buildings. The value for the latter would depend on the structure of the building, its environment while the values for the former will be more less roughly the same. We could thus categorize all images whose Haar-like feature in this rectangular region to be in a certain range of values as one category and those falling out of this range in another. This might roughly divide the set of images into ones having a lot of faces and a few buildings and the other having a lot of buildings and a few faces. This procedure could be iteratively carried out to further divide the image clusters.

A slightly more complicated feature is to break the rectangular image to either a left half and a right half or a top half and a bottom half and trace the difference in the sum of pixels across these halves. This modified feature set is called 2 rectangle feature.