Robust object recognition with cortex-like mechanisms

Thomas Serre*, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, Tomaso Poggio

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1318 Scopus citations

Abstract

We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both shape-based as well as texture-based objects. Given the biological constraints that the system had to satisfy, the approach performs surprisingly well: It has the capability of learning from only a few training examples and competes with state-of-the-art systems. We also discuss the existence of a universal, redundant dictionary of features that could handle the recognition of most object categories. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object recognition in cortex.

Original languageEnglish
Pages (from-to)411-426
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume29
Issue number3
DOIs
StatePublished - Mar 2007
Externally publishedYes

Funding

FundersFunder number
Eugene McDermott Foundation
Honda Research Institute USA, Inc.
Komatsu Ltd.
Sumitomo Metal Industries
US Office of Naval Research
Defense Advanced Research Projects Agency
Eastman Kodak
Toyota Motor Corporation

    Keywords

    • Model
    • Neural network
    • Object recognition
    • Scene understanding
    • Visual cortex

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