A New Biologically Motivated Framework for Robust Object Recognition

Abstract : In this paper,we introduce a novel set of features for robust object recognition, which exhibits outstanding performances on a variety of object categories while being capable of learning from only a few training examples. Each element of this set is a complex feature obtained by combining position- and scale-tolerant edge-detectors over neighboring positions and multiple orientations. Our system motivated by a quantitative model of visual cortex outperforms state-of-the-art systems on a variety of object image datasets from different groups. We also show that our system is able to learn from very few examples with no prior category knowledge. The success of the approach is also a suggestive plausibility proof for a class of feed-forward models of object recognition in cortex. Finally, we conjecture the existence of a universal overcomplete dictionary of features that could handle the recognition of all object categories.