Diagnostic Colors Mediate Scene Recognition

Oliva, Aude; Schyns, Philippe G.

doi:10.1006/cogp.1999.0728

Cited by 372 publications

(309 citation statements)

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“…Participants had an 81.8% correct recognition rate when color images were presented at a resolution of 32x32 pixels. In out study, performance over 80% is found for an image resolution slightly higher than the one reported in Oliva & Schyns (2000), who used a Gaussian filter and measured the frequency of 50% cut-off instead of the number of pixels in the downsampled image. For grayscale images, performance was at 73% at 32x32 pixels.…”

Section: Resultsmentioning

confidence: 64%

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How many pixels make an image?

2009

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The human visual system is remarkably tolerant to degradation in image resolution: human performance in scene categorization remains high no matter whether low resolution images or multi-mega pixel images are used. This observation raises the question of how many pixels are required to form a meaningful representation of an image and identify the objects it contains. In this paper, we show that very small thumbnail images at the spatial resolution of 32x32 color pixels provide enough information to identify the semantic category of real world scenes. Most strikingly, this low resolution permits observers to report, with 80% accuracy, 4-5 of the objects that the scene contains, despite the fact that some of these objects are unrecognizable in isolation. The robustness of the information available at very low resolution for describing semantic content of natural images could be an important asset to explain the speed and efficiently at which the human brain comprehends the gist of visual scenes.

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Section: Resultsmentioning

confidence: 64%

“…identity, gender, emotion). Remarkable performance with low resolution pictures is also found on scene recognition tasks (Oliva & Schyns, 2000;Castelhano & Henderson, 2008).…”

Section: Introductionmentioning

confidence: 69%

How many pixels make an image?

2009

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“…These image statistics characterize different properties of image luminance. The luminance of each scene was extracted by converting the scene's RGB values to the CIE L * a * b * colorspace (Oliva & Schyns, 2000) which separates the luminance information of an image into a distinct dimension (L * . The chromatic information in the a * and b * dimensions was discarded, and analyses focused on the values in the L * dimension.…”

Section: Scene Statistics Methodsmentioning

confidence: 99%

Visual saliency does not account for eye movements during visual search in real-world scenes

Henderson

Brockmole

Castelhano³

et al. 2007

Eye Movements

341

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“…longer in the grayscale and abnormal conditions than in the normal condition. Oliva and Schyns (2000) explored the influence of color information with color-diagnostic images and colornondiagnostic images. As with Goffaux et al (2005), normal, grayscale, and abnormal conditions were compared as a function of color diagnosticity.…”

Section: Sachio Otsuka and Jun Kawaguchimentioning

confidence: 99%