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A new biologically motivated framework for robust object recognition

Author(s)
Serre, Thomas; Wolf, Lior; Poggio, Tomaso
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Abstract
In this paper, we introduce a novel set of features for robust object recognition, which exhibits outstanding performances on a variety ofobject categories while being capable of learning from only a fewtraining examples. Each element of this set is a complex featureobtained by combining position- and scale-tolerant edge-detectors overneighboring positions and multiple orientations.Our system - motivated by a quantitative model of visual cortex -outperforms state-of-the-art systems on a variety of object imagedatasets from different groups. We also show that our system is ableto learn from very few examples with no prior category knowledge. Thesuccess of the approach is also a suggestive plausibility proof for aclass of feed-forward models of object recognition in cortex. Finally,we conjecture the existence of a universal overcompletedictionary of features that could handle the recognition of all objectcategories.
Date issued
2004-11-14
URI
http://hdl.handle.net/1721.1/30504
Other identifiers
MIT-CSAIL-TR-2004-074
AIM-2004-026
CBCL-243
Series/Report no.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
Keywords
AI, visual cortex, object recognition, face detection, hierarchy, feature learning

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