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Object recognition with features inspired by visual cortex

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3 Author(s)
T. Serre ; Dept. of Brain & Cognitive Sci., MIT, Cambridge, MA, USA ; L. Wolf ; T. Poggio

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.

Published in:

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)  (Volume:2 )

Date of Conference:

20-25 June 2005