This work is motivated by remarkable success of many method using histogram based image representations: [Swain & Ballard 1991] [Schiele & Crowley 1996] [Lowe 1999] [Schneiderman & Kanade 2000]
[Leung & Malik 2001] [Belongie et.al. 2002]
[Dalal & Triggs 2005]
We focus on selecting support regions of histogram image features for a given object class. As illustrated on the left, information within different object regions may vary in terms of discriminative power (regions "A" are more discriminative than regions "D"). Similar to [Levi & Weiss 2004] we use AdaBoost to select histogram regions optimized for the classification of training samples.