Visual features of intermediate complexity and their use in classification
Abstract
The human visual system analyzes shapes and objects in a series of stages in which stimulus features of increasing complexity are extracted and analyzed. The first stages use simple local features, and the image is subsequently represented in terms of larger and more complex features. These include features of intermediate complexity and partial object views. The nature and use of these higher-order representations remains an open question in the study of visual processing by the primate cortex. Here we show that intermediate complexity (IC) features are optimal for the basic visual task of classification. Moderately complex features are more informative for classification than very simple or very complex ones, and so they emerge naturally by the simple coding principle of information maximization with respect to a class of images. Our findings suggest a specific role for IC features in visual processing and a principle for their extraction.
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Coarse-grained optimization involves developing letter combination detectors that best constrain word identity, the diagnosticity constraint.
“Here, optimization is thought to involve the development of letter combination detectors that best constrain word identity (the diagnosticity constraint), in the same way that parts of objects act as clues to object identity in certain theoretical approaches to visual object recognition (e.g., Ullman et al., 2002).”
From A Dual-Route Approach to Orthographic Processing (2011) · cites this paper as Ullman et al., 2002
Our results agree with several theories of visual processing that suggest that an initial feedforward sweep driven by bottom-up inputs builds a base representation that relies on a basic dictionary of generic features before more complex tasks or visual routines can take place through recurrent projections from higher areas.
“Our results indeed agree with several theories of visual processing that suggest that an initial feedforward sweep driven by bottom-up inputs builds a base representation that relies on a basic dictionary of generic features (11-13, 17, 41) before more complex tasks or visual routines can take place through recurrent projections from higher areas (20, 42, 43, 45).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [17]
Key to the recognition performance of the model is the large number of tuned units across its hierarchical architecture, which is a direct consequence of learning from natural images and represents a redundant dictionary of fragment-like features that span a range of selectivities and invariances.
“Key to the recognition performance of the model is the large number of tuned units across its hierarchical architecture, which is a direct consequence of the learning from natural images and represent a redundant dictionary of fragment-like features (12, 17, 41) that span a range of selectivities and invariances.”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [17]
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- Authors
- Ullman, S., Vidal-Naquet, M., & Sali, E.
- Affiliations
- Weizmann Institute of Science (IL)
- Corresponding author
- Ullman, Shimon
- ORCID
- Ullman, Shimon
- Venue
- Nature Neuroscience
- Published
- 2002-06-10
- Volume
- 5
- Issue
- 7
- Pages
- 682-687
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2037090920
- Cited by
- 600
- References
- 30
- Citations by year
- 2025: 6 · 2024: 10 · 2023: 10 · 2022: 13 · 2021: 17 · 2020: 9 · 2019: 15 · 2018: 10 · 2017: 17 · 2016: 20 · 2015: 33 · 2014: 30 · 2013: 24 · 2012: 39
- FWCI
- 14.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- 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 › Visual Attention and Saliency Detection
- Keywords
- visual feature extraction, object classification, information maximization, feature complexity, visual cortex, hierarchical visual processing, primate visual system
- MeSH terms
- Computer Simulation, Face, Humans, Image Processing, Computer-Assisted, Pattern Recognition, Visual
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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