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Models of object recognition

Nature Neuroscience volume 3, pages 1199–1204 (2000)Cite this article

Abstract

Understanding how biological visual systems recognize objects is one of the ultimate goals in computational neuroscience. From the computational viewpoint of learning, different recognition tasks, such as categorization and identification, are similar, representing different trade-offs between specificity and invariance. Thus, the different tasks do not require different classes of models. We briefly review some recent trends in computational vision and then focus on feedforward, view-based models that are supported by psychophysical and physiological data.

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Figure 1: Learning module schematics.
Figure 2
Figure 3: A class of models of object recognition.

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Acknowledgements

Supported by grants from ONR, DARPA, NSF, ATR, Honda, a Merck/MIT Fellowship in Bioinformatics, and a McDonnell Pcw award (M.R.). T.P. is supported by the Uncas and Helen Whitaker Chair at the Whitaker College, MIT. For comments and suggestions, we are grateful to Heinrich Bülthoff, Peter Dayan, Shimon Edelman, David Freedman, Christof Koch, Earl Miller, David Perrett, Pawan Sinha and Francis Crick (also for the picture in Fig. 3).

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  1. Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Center for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, 02142, Massachusetts, USA

    Maximilian Riesenhuber & Tomaso Poggio

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  1. Maximilian Riesenhuber
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Correspondence to Tomaso Poggio.

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Riesenhuber, M., Poggio, T. Models of object recognition. Nat Neurosci 3 (Suppl 11), 1199–1204 (2000). https://doi.org/10.1038/81479

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