Object recognition and detection represent a relevant component in cognitive computer vision systems, such as in robot vision, intelligent video surveillance systems, or multi-modal interfaces. Object identification from local information has recently been investigated with respect to its potential for robust recognition, e.g., in case of partial object occlusions, scale variation, noise, and background clutter in detection tasks. This work contributes to this research by a thorough analysis of the discriminative power of local appearance patterns and by proposing to exploit local information content to model object representation and recognition. We identify discriminative regions in the object views from a posterior entropy measure, and then derive object models from selected discriminative local patterns. For recognition, we determine rapid attentive search for locations of high information content from learned decision trees. The recognition system is evaluated by various degrees of partial occlusion and Gaussian image noise, resulting in highly robust recognition even in the presence of severe occlusion effects.