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Cited by 3,309Visual perception and processing mechanismsFace Recognition and PerceptionAdvanced Image and Video Retrieval Techniques

Hierarchical models of object recognition in cortex

Riesenhuber, Maximilian, Poggio, Tomaso·1999·Nature Neuroscience
DOI 10.1038/14819
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Abstract

Visual processing in cortex is classically modeled as a hierarchy of increasingly sophisticated representations, naturally extending the model of simple to complex cells of Hubel and Wiesel. Surprisingly, little quantitative modeling has been done to explore the biological feasibility of this class of models to explain aspects of higher-level visual processing such as object recognition. We describe a new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions. The model is based on a MAX-like operation applied to inputs to certain cortical neurons that may have a general role in cortical function.

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Crossref · 10.1038/14819

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54 of 54 references verified0 citation errors · 0 not checkable

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  • Visual systems have a convolutional nature (the same computations are applied to each pixel of the visual input).

    “One hallmark of visual systems that has been widely exploited in such tasks is their convolutional nature66–69 (that is, the fact that the same computations are applied to each pixel of the visual input).”

    From Connectome-constrained networks predict neural activity across the fly visual system (2024) · cites this paper as [68]

  • The computational requirements for invariant recognition are largely common to the recognition of any type of visual objects.

    “Those computational requirements are largely common to the recognition of any type of visual objects: Tools, buildings, or faces can all appear with huge variations in their appearance, including size, location, orientation, and so on.”

    From Specialization within the ventral stream: the case for the visual word form area (2004) · cites this paper as Riesenhuber and Poggio, 1999

  • From V1 to IT, there is an increase in invariance to position and scale and, in parallel, an increase in the size of the receptive fields and in the complexity of the optimal stimuli for the neurons.

    “From V1 to IT, there is an increase in invariance to position and scale (1, 2, 4-6) and in parallel, an increase in the size of the receptive fields (2, 4) as well as in the complexity of the optimal stimuli for the neurons (2, 3, 7).”

    From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [5]

  • Plausible biophysical circuits may implement the two key operations assumed by the theory within the time constraints of the experimental data.

    “In addition, plausible biophysical circuits may implement the two key operations (5) assumed by the theory within the time constraints of the experimental data (8).”

    From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [5]

  • The model accounts for the bypass routes from V2 to the posterior inferotemporal cortex and from V4 to the anterior inferotemporal cortex, unlike the original model.

    “In addition to the main routes through the V4 to the IT cortex (4), the model also accounts for the bypass routes (38) from V2 to the posterior inferotemporal cortex and from V4 to the anterior inferotemporal cortex (Fig. 1) [unlike the original model (5)].”

    From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [5]

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Authors
Riesenhuber, M., & Poggio, T.
Affiliations
Massachusetts Institute of Technology (US)
Corresponding author
Poggio, Tomaso
ORCID
Venue
Nature Neuroscience
Publisher
Springer Science and Business Media LLC
Published
1999-11-01
Volume
2
Issue
11
Pages
1019-1025
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
DOI
10.1038/14819
OpenAlex
W2149194912
Cited by
3,309
References
54
Citations by year
2026: 68 · 2025: 86 · 2024: 91 · 2023: 100 · 2022: 117 · 2021: 167 · 2020: 152 · 2019: 159 · 2018: 136 · 2017: 156 · 2016: 195 · 2015: 193 · 2014: 233 · 2013: 200 · 2012: 217
FWCI
24.12
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Nature Neuroscience (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
object recognition, hierarchical models, inferotemporal cortex, cortical neurons, biological plausibility, visual cortex
MeSH terms
Animals, Computer Simulation, Form Perception, Macaca, Models, Neurological, Neurons, Mental Recall, Visual Cortex, Visual Fields

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