In this paper, we propose a new context-based method
for object recognition. We first introduce a neurophysiologically
motivated visual part detector. We found
that the optimal form of the visual part detector is a combination
of a radial symmetry detector and a corner-like
structure detector. A general context descriptor, named GRIF
(Generalized-Robust Invariant Feature), is then proposed,
which encodes edge orientation, edge density and
hue information in a unified form. Finally, a context-based
voting scheme is proposed. This proposed method is inspired
by the function of the human visual system, called
figure-ground discrimination. We use the proximity and
similarity between features to support each other. The contextual
feature descriptor and contextual voting method,
which use contextual information, enhance the recognition
performance enormously in severely cluttered environments.