In this work we introduce a hierarchical representation for object detection. We represent an object in terms of parts composed of contours corresponding to object boundaries and symmetry axes; these are in turn related to edge and ridge features that are extracted from the image. We propose a coarse-to-fine algorithm for efficient detection which exploits the hierarchical nature of the model. This provides a tractable framework to combine bottom-up and top-down computation. We learn our models from training images where only the bounding box of the object is provided. We automate the decomposition of an object category into parts and contours, and discriminatively learn the cost function that drives the matching of the object to the image using Multiple Instance Learning. Using shape-based information, we obtain state-of-theart localization results on the UIUC and ETHZ datasets.
Kokkinos and Yuille REF first decompose object categories into parts and shape contours using a top-down approach.
Inference and Learning with Hierarchical Shape Models
International Journal of Computer Vision
International Journal of Computer Vision
Computer Science