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Texture Interpolation for Probing Visual Perception.

Jonathan Vacher1, Aida Davila2, Adam Kohn3

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Summary
This summary is machine-generated.

This study reveals that constraining the mean and covariance of deep convolutional neural network (CNN) activations is sufficient for texture synthesis. The proposed geodesic interpolation method aligns better with human texture perception and neural sensitivity.

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Area of Science:

  • Computational Neuroscience
  • Computer Vision
  • Machine Learning

Background:

  • Statistical models using neurally relevant features have advanced understanding of visual perception and neural coding.
  • Deep learning methods, particularly deep convolutional neural networks (CNNs), have significantly improved synthetic texture quality.
  • The underlying mechanisms of deep texture synthesis performance and its application to visual perception remain underexplored.

Purpose of the Study:

  • To demonstrate that elliptical distributions well describe CNN activation patterns in textures.
  • To introduce a novel texture interpolation method based on optimal transport theory and natural geodesics.
  • To investigate the alignment of this new method with the geometry of human texture perception and neural sensitivity.

Main Methods:

  • Modeling texture features using elliptical distributions of deep convolutional neural network (CNN) activations.
  • Applying optimal transport theory to constrain mean and covariance for generating new texture samples.
  • Developing a natural geodesic interpolation method between textures using the optimal transport metric.

Main Results:

  • Texture activation distributions in deep CNNs are accurately described by elliptical distributions.
  • Constraining mean and covariance of these activations is sufficient for high-quality texture synthesis.
  • The proposed geodesic interpolation method shows closer alignment with the geometry of texture perception than existing CNN-based approaches.

Conclusions:

  • Deep texture synthesis can be mathematically framed by constraining the statistical properties (mean and covariance) of CNN activations.
  • Natural geodesics under the optimal transport metric provide a robust framework for texture interpolation.
  • The method offers a valuable tool for studying the statistical nature of visual perception and measuring perceptual scales and neural sensitivity.