Object recognition with features inspired by visual cortex

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 2; pp. 994 - 1000 vol. 2
• Main Authors: Serre, T., Wolf, L., Poggio, T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Biology computing; Brain modeling; Face detection; Geometry; Image recognition; Object detection; Object recognition; Robustness; Shape; Target recognition
• DOI: 10.1109/CVPR.2005.254
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919

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 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.