Integrating spatial layout of object parts into classification without pairwise terms: application to fast body parts estimation from depth images

Abstract : Object recognition or human pose estimation methods often resort to a decomposition into a collection of parts. This local representation has significant advantages, especially in case of occlusions and when the ``object'' is non-rigid. Detection and recognition requires modelling the appearance of the different object parts as well as their spatial layout. The latter can be complex and requires the minimization of complex energy functions, which is prohibitive in most real world applications and therefore often omitted. However, ignoring the spatial layout puts all the burden on the classifier, whose only available information is local appearance. We propose a new method to integrate the spatial layout into the parts classification without costly pairwise terms. We present an application to body parts classification for human pose estimation.As a second contribution, we introduce edge features from RGB images as a complement to the well known depth features used for body parts classification from Kinect data.