Object Recognition with Features Inspired by Visual Cortex

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 edgedetectors over neighboring positions and multiple orientations. Our system's architecture is motivated by a quantitative model of visual cortex. We show that our approach exhibits excellent recognition performance and outperforms several state-of-the-art systems on a variety of image datasets including many different object categories. We also demonstrate that our system is able to learn from very few examples. The performance of the approach constitutes a suggestive plausibility proof for a class of feedforward models of object recognition in cortex.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Thomas Serre, Lior Wolf, Tomaso Poggio
• Keywords: Computer Vision, CVPR 2005, Feedforward Models, Robust Object Recognition, Suggestive Plausibility Proof, Visual Cortex