In this paper, we present a new, biologically inspired perceptual
feature to solve the selectivity and invariance issue in object recognition.
Based on the recent findings in neuronal and cognitive mechanisms
in human visual systems, we develop a computationally efficient model.
An effective form of a visual part detector combines a radial symmetry
detector with a corner-like structure detector. A general context descriptor
encodes edge orientation, edge density, and hue information using a
localized receptive field histogram. We compare the proposed perceptual
feature (G-RIF: generalized robust invariant feature) with the state-ofthe-
art feature, SIFT, for feature-based object recognition. The experimental
results validate the robustness of the proposed perceptual feature
in object recognition.