A variety of flexible models have been proposed to detect
objects in challenging real world scenes. Motivated
by some of the most successful techniques, we propose a
hierarchical multi-feature representation and automatically
learn flexible hierarchical object models for a wide variety
of object classes. To that end we not only rely on automatic
selection of relevant individual features, but go beyond previous
work by automatically selecting and modeling complex,
long-range feature couplings within this model. To
achieve this generality and flexibility our work combines
structure learning in conditional random fields and discriminative
parameter learning of classifiers using hierarchical
features. We adopt an efficient gradient based heuristic for
model selection and carry it forward to discriminative, multidimensional
selection of features and their couplings for
improved detection performance. Experimentally we consistently
outperform the currently leading method on a...