T.Poggio Object recognition with Features inspired by Visual Cortex CVPR05

We introduce a novel set of features for robust object recognition. Each element of this set is a complex feature obtained by combining position- and scale-tolerant edge-detectors over neighboring positions and multiple orienta-tions. Our system’s architecture is motivated by a quantita-tive model of visual cortex. We show that our approach exhibits excellent recogni-tion performance and outperforms several state-of-the-art systems on a variety of image datasets including many dif-ferent object categories. We also demonstrate that our sys-tem is able to learn from very few examples. The perfor-mance of the approach constitutes a suggestive plausibility proof for a class of feedforward models of object recogni-tion in cortex.