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Object Recognition with Features Inspired by Visual Cortex

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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.
Thomas Serre, Lior Wolf, Tomaso Poggio
Added 12 Oct 2009
Updated 08 Jul 2010
Type Conference
Year 2005
Where CVPR
Authors Thomas Serre, Lior Wolf, Tomaso Poggio
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