Robust Object Recognition with Cortex-Like Mechanisms
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.
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This class of feedforward models can duplicate the tuning properties of at least some cortical cells when probed with artificial stimuli and can handle the recognition of objects in the real world where objects may undergo drastic changes in appearance.
“Not only does this class of feedforward models seem to be able to duplicate the tuning properties of at least some cortical cells when probed with artificial stimuli, but it can also handle the recognition of objects in the real world (39, 40) where objects may undergo drastic changes in appearance (e.g., clutter, shape, illumination).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [39]
In agreement with the human data, the model is able to recognize objects with limited clutter.
“In agreement with the human data, the model is able to recognize objects with limited clutter (see ref. 39 for results on a large database of 101 object categories).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [39]
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- Authors
- Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., & Poggio, T.
- Affiliations
- McGovern Institute for Brain Research (US)
- Georgetown University Medical Center (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-03
- Volume
- 29
- Issue
- 3
- Pages
- 411-426
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2144982973
- Semantic Scholar
- 71e3d9fc53ba14c2feeb7390f0dc99076553b05a
- Cited by
- 1,667
- References
- 76
- Citations by year
- 2026: 12 · 2025: 31 · 2024: 43 · 2023: 38 · 2022: 41 · 2021: 63 · 2020: 65 · 2019: 70 · 2018: 69 · 2017: 74 · 2016: 114 · 2015: 129 · 2014: 161 · 2013: 123 · 2012: 134
- FWCI
- 152.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- object recognition, hierarchical feature representation, template matching, maximum pooling, biological plausibility, feature dictionary, feedforward models, invariance
- MeSH terms
- Algorithms, Artificial Intelligence, Computer Simulation, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Models, Biological, Pattern Recognition, Automated, Pattern Recognition, Visual, Sensitivity and Specificity, Visual Cortex, Reproducibility of Results, Biomimetics
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