A Comparison of Features in Parts-based Object Recognition Hierarchies

Parts-based recognition has been suggested for generalizing from few training views in categorization scenarios. In this paper we present the results of a comparative investigation of different feature types with regard to their suitability for category discrimination. So patches of gray-scale images were compared with SIFT descriptors and patches from the high-level output of a feedforward hierarchy related to the ventral visual pathway. We discuss the conceptual differences, resulting performance and consequences for hierarchical models of visual recognition.