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I think you are mixing things up.
HAAR-like wavelets are always rectangular features, which take areas of pixels and subtract those values. The end result is then tresholded to make a decision for a specific feature. More information on which type of haar-like wavelets can be found here. OpenCV supports the use of the basic orientations, but it also supports use of the rotated haar-like wavelets.
The Viola & Jones framework supports the use of this features. Depending on how the rectangles are oriented, one could say they search for specific behaviour, namely gradient information. A strong gradient in vertical direction, will make that there is a large difference between two vertical boxes moving over it.
This is why these features are stated as features looking for edges/lines, which is basically the same. The four rectangleqs type of features go looking for more specific information and gradient structures, like corners and such.
Basically read the Viola&Jones paper for more information on them.
