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Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.

Groen II, Ghebreab S, Lamme VA, Scholte HS - PLoS Comput. Biol. (2012)

Bottom Line: We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis).Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions.These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

View Article: PubMed Central - PubMed

Affiliation: Cognitive Neuroscience Group, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

ABSTRACT
The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

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Contrast histograms of natural images follow a Weibull distribution.(A), Three natural images with varying degrees of details and scene fragmentation. The homogenous, texture-like image of grass (upper row) contains many edges of various strengths; its contrast distribution approaches a Gaussian. The strongly segmented image of green leaves against a uniform background (bottom row) contains very few, strong edges that are highly coherent; its distribution approaches power law. Most natural images, however, have distributions in between (middle row). The degree to which images vary between these two extremes is reflected in the free parameters of a Weibull fit to the contrast histogram: β (beta) and γ (gamma). (B), For each of 200 natural scenes, the beta and gamma values were derived from fitting the Weibull distribution to their contrast histogram. Beta describes the width of the histogram: it varies with the distribution of local contrasts strengths. Gamma describes the shape of the histogram: it varies with the amount of scene clutter. Four representative pictures are shown in each corner of the parameter space. Images with a high degree of scene segmentation, e.g. a leaf on top of snow, are found in the lower left corner, whereas highly cluttered images are on the right. Images with more depth are located on the top, whereas flat images are found at the bottom. Images are from the McGill Calibrated Colour Image Database [86].
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pcbi-1002726-g001: Contrast histograms of natural images follow a Weibull distribution.(A), Three natural images with varying degrees of details and scene fragmentation. The homogenous, texture-like image of grass (upper row) contains many edges of various strengths; its contrast distribution approaches a Gaussian. The strongly segmented image of green leaves against a uniform background (bottom row) contains very few, strong edges that are highly coherent; its distribution approaches power law. Most natural images, however, have distributions in between (middle row). The degree to which images vary between these two extremes is reflected in the free parameters of a Weibull fit to the contrast histogram: β (beta) and γ (gamma). (B), For each of 200 natural scenes, the beta and gamma values were derived from fitting the Weibull distribution to their contrast histogram. Beta describes the width of the histogram: it varies with the distribution of local contrasts strengths. Gamma describes the shape of the histogram: it varies with the amount of scene clutter. Four representative pictures are shown in each corner of the parameter space. Images with a high degree of scene segmentation, e.g. a leaf on top of snow, are found in the lower left corner, whereas highly cluttered images are on the right. Images with more depth are located on the top, whereas flat images are found at the bottom. Images are from the McGill Calibrated Colour Image Database [86].

Mentions: The fact that it is mathematically possible to distinguish categories based on image statistics, however, does not imply that they are used for categorization in the brain. Image statistics may not be sufficiently reliable, or their computation may not be suitable for neural implementation [12], [16]. We recently showed that statistics derived from the frequency histogram of local contrast – summarized by two parameters of a Weibull fit, Fig. 1A – explain up to 50% of the variance of event-related potentials (ERPs) recorded from visual cortex [17]. These parameters inform about the width and shape of the histogram, respectively, and appear to describe meaningful variability between images (Fig. 1B). Importantly, we found that these parameters can be reliably approximated by linear summation of the output of localized difference-of-Gaussians filters modeled after X- and Y-type LGN cells, suggesting that this global information may be available to visual cortex directly from its early low-level contrast responses [17].


Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.

Groen II, Ghebreab S, Lamme VA, Scholte HS - PLoS Comput. Biol. (2012)

Contrast histograms of natural images follow a Weibull distribution.(A), Three natural images with varying degrees of details and scene fragmentation. The homogenous, texture-like image of grass (upper row) contains many edges of various strengths; its contrast distribution approaches a Gaussian. The strongly segmented image of green leaves against a uniform background (bottom row) contains very few, strong edges that are highly coherent; its distribution approaches power law. Most natural images, however, have distributions in between (middle row). The degree to which images vary between these two extremes is reflected in the free parameters of a Weibull fit to the contrast histogram: β (beta) and γ (gamma). (B), For each of 200 natural scenes, the beta and gamma values were derived from fitting the Weibull distribution to their contrast histogram. Beta describes the width of the histogram: it varies with the distribution of local contrasts strengths. Gamma describes the shape of the histogram: it varies with the amount of scene clutter. Four representative pictures are shown in each corner of the parameter space. Images with a high degree of scene segmentation, e.g. a leaf on top of snow, are found in the lower left corner, whereas highly cluttered images are on the right. Images with more depth are located on the top, whereas flat images are found at the bottom. Images are from the McGill Calibrated Colour Image Database [86].
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC3475684&req=5

pcbi-1002726-g001: Contrast histograms of natural images follow a Weibull distribution.(A), Three natural images with varying degrees of details and scene fragmentation. The homogenous, texture-like image of grass (upper row) contains many edges of various strengths; its contrast distribution approaches a Gaussian. The strongly segmented image of green leaves against a uniform background (bottom row) contains very few, strong edges that are highly coherent; its distribution approaches power law. Most natural images, however, have distributions in between (middle row). The degree to which images vary between these two extremes is reflected in the free parameters of a Weibull fit to the contrast histogram: β (beta) and γ (gamma). (B), For each of 200 natural scenes, the beta and gamma values were derived from fitting the Weibull distribution to their contrast histogram. Beta describes the width of the histogram: it varies with the distribution of local contrasts strengths. Gamma describes the shape of the histogram: it varies with the amount of scene clutter. Four representative pictures are shown in each corner of the parameter space. Images with a high degree of scene segmentation, e.g. a leaf on top of snow, are found in the lower left corner, whereas highly cluttered images are on the right. Images with more depth are located on the top, whereas flat images are found at the bottom. Images are from the McGill Calibrated Colour Image Database [86].
Mentions: The fact that it is mathematically possible to distinguish categories based on image statistics, however, does not imply that they are used for categorization in the brain. Image statistics may not be sufficiently reliable, or their computation may not be suitable for neural implementation [12], [16]. We recently showed that statistics derived from the frequency histogram of local contrast – summarized by two parameters of a Weibull fit, Fig. 1A – explain up to 50% of the variance of event-related potentials (ERPs) recorded from visual cortex [17]. These parameters inform about the width and shape of the histogram, respectively, and appear to describe meaningful variability between images (Fig. 1B). Importantly, we found that these parameters can be reliably approximated by linear summation of the output of localized difference-of-Gaussians filters modeled after X- and Y-type LGN cells, suggesting that this global information may be available to visual cortex directly from its early low-level contrast responses [17].

Bottom Line: We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis).Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions.These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

View Article: PubMed Central - PubMed

Affiliation: Cognitive Neuroscience Group, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

ABSTRACT
The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

Show MeSH
Related in: MedlinePlus