public repository of data and code for CNN texture appearance paper
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readme.md

Code and data for "A parametric texture model based on deep convolutional features closely matches texture appearance for humans" by Wallis et al.

DOI

This repository contains the code necessary to reproduce the results of

Wallis, Funke, Ecker, Gatys, Wichmann and Bethge (in press). A parametric texture model based on deep convolutional features closely matches texture appearance for humans. Journal of Vision.

A preprint corresponding to the paper can be found at:

https://doi.org/10.1101/165761

Stimuli can be found in a complementary archive (http://doi.org/10.5281/zenodo.438031; stored separately due to license restrictions on images).

Directory structure of this repository

  • code = code for generating stimuli, running experiment, and analysing data.
  • documentation = some testing protocols and subject demographics
  • figures = some figures from the paper (data figures produced by the knitr document)
  • raw_data = raw data from the experiments.
  • results = processed data from the experiments

Because the fitted brms/Stan RData files were too large for git, they are attached to the release as a .zip file. To knit the document you should place these into the appropriate results subdirectory (i.e. results/experiment_11/ and results/experiment_12).

The article

The manuscript was produced using knitr (Xie, 2013; 2015). The main file is texture_discrimination_manuscript.Rnw. You can look in here for the bare text of the manuscript, plus all the associated R code for generating figures and the numbers in the paper.

With all appropriate packages installed, "knitting" this will produce

  • a .tex file texture_discrimination_manuscript.tex
  • a .pdf file texture_discrimination_manuscript.pdf
  • a subdirectory cache/, featuring cached results
  • a subdirectory figure/, featuring features automagically generated by the embedded R code.

Xie, Y. (2013). knitr: A comprehensive tool for reproducible research in R. BT - Implementing Reproducible Computational Research. In V. Stodden, F. Leisch, & R. D. Peng (Eds.), Implementing Reproducible Computational Research. Chapman & Hall/CRC.

Xie, Y. (2015). Dynamic Documents with R and Knitr, Second Edition (2nd ed.). Chapman & Hall/CRC.

I looked into using packrat for this project but had troubles with some library compilation (incompatible clang; apparently an OSX problem). I hope the version numbers reported in the paper suffice.

Generating new stimuli

The code to generate stimuli from the CNN texture model can be found at https://github.com/leongatys/DeepTextures

Experiment labelling

Note that Experiment 1 from the paper corresponds to experiment_11 in the code and Experiment 2 in the paper corresponds to experiment_12 in the code. We ran related but separate experiments that were stored in the same repository. I've kept the code as-is to reduce the need to relabel directories.

To run the code in your own setup you will need to adjust top directory paths to the relevant path on your local machine. Look for the variable top_dir in most scripts; that should suffice. If you know of a nicer way to handle this please let me know (RStudio projects work for the bulk, but will create a different working directory than for knitting documents).