On the role of structure in part-based object detection
Part-based approaches in image analysis aim at exploiting the considerable discriminative power embedded in relations among image parts. Nonetheless, learning structural information is not always possible without the availability of a training set of classified parts, and taking into account this additional information can even degrade the performance of the system. In this paper, a discriminative graphical model for object detection is introduced and used in order to analyse and report results on the role of structural information in image classification tasks.