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Related Concept Videos

Color Vision01:24

Color Vision

Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
Perceptual Constancy01:12

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Related Experiment Video

Updated: Jun 10, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

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Published on: May 7, 2019

Evaluating color descriptors for object and scene recognition.

Koen E A van de Sande1, Theo Gevers, Cees G M Snoek

  • 1Informatics Institute, University of Amsterdam, Science Park 107, 1098 XG Amsterdam, The Netherlands. ksande@uva.nl

IEEE Transactions on Pattern Analysis and Machine Intelligence
|July 17, 2010
PubMed
Summary
This summary is machine-generated.

This study evaluates color invariant descriptors for image category recognition. OpponentSIFT is recommended when no prior data knowledge is available, outperforming intensity-based methods.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Intensity-based descriptors are common for image feature extraction.
  • Color descriptors offer improved illumination invariance and discriminative power.
  • A structured overview of color invariant descriptors is needed for image category recognition.

Purpose of the Study:

  • To systematically study the invariance properties and distinctiveness of color descriptors.
  • To analyze descriptor performance under varying photometric transformations.
  • To assess descriptor effectiveness in image and video category recognition benchmarks.

Main Methods:

  • Explored analytical invariance properties using a taxonomy based on photometric transformations.
  • Experimentally tested invariance using a dataset with known illumination conditions.
  • Assessed descriptor distinctiveness using image and video domain benchmarks.

Main Results:

  • Invariance to light intensity and color changes significantly impacts category recognition.
  • The utility of invariance for light intensity shifts is category-specific.
  • OpponentSIFT is recommended as a single descriptor without prior data knowledge.

Conclusions:

  • A combined set of color descriptors enhances category recognition over intensity-based SIFT.
  • Performance improvements of 8% (PASCAL VOC 2007) and 7% (Mediamill Challenge) were observed.
  • The choice of descriptor should consider illumination invariance and category specificity.