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This repository contains the replication files for article "Image Clustering: An Unsupervised Approach to Categorize Visual Data in Social Science Research."

Data access

This project uses three datasets.

  • Study 1's dataset can be accessed at Harvard Dataverse.
  • Study 2 uses two datasets.
    • The first dataset can be accessed via the above same link. You can also download the images directly from Instagram.
    • The second dataset can be requested directly from Twitter link. Please choose the batch released in October 2018.

Study 1:

Step 1: extract intermediate low-dimensional vector representation of Study 1(China Protest) and store the features

  • study1_build: navigate to this folder and follow the below steps
    • images/: 14,127 images used in Study 1 of the manuscript. You should request access to this dataset from the above dataset link and rename the folder as images/
    • bovw/: bag-of-visual-word model.
      • navigate to this folder and run extract_bbow_features.py :extract intermediate low-dimensional vector representation and save it to protest_bbow.csv
    • self-supervised/ : self-supervised algorithm based on DeepCluster (Caron et al., 2018). The code was based on the original coding implmenetation at https://github.com/facebookresearch/deepcluster
      • Run the below two .sh files. Note that when running these two files, it will first download several large pre-trained models (over GB) and then save the training results as checkpoints. So you should have at least 5 GB remaining on your disk to successfully run these scripts. The two .sh files will save extracted features in files with extension .pickle. These are binary format vectors
        • train_self_supervised_from_scratches.sh: extract intermediate low-dimensional vector representation using Deep Cluster from scratch.
        • train_self_supervised_transfer.sh: use the DeepCluster but did not train the model by yourself; instead, this script performs transfer learning by using their model to extract the last layer of the CNN model and use that layer as the intermediate representation.
    • supervised/ : transfer learning based on VGG and ImageNet dataset.
      • run extract_features.py to extract intermediate low-dimensional vector representation. The extracted features will be saved to "img transform/" folder.

Step 2: run clustering algorithms over extracted intermediate vector representations to obtain label of each image.

  • clustering.py : this python script will run clustering algorithms over all the previous extracted intermediate representations.

Step 3: visualization and validation

  • Fig1/ ; Fig 4/ ; .... All these folders contain one python script. Run the script and you will get corresponding figure or tables used in the manuscript.

Study 2A

A1/2/3 feature extraction.py

There three files extract features from three pre-trained models and save features in the "img exfeature" folder. Please make sure to change the folder path in the scripts. If you want to use the places365 model, please download the model from this link and place all the relevant scripts in the same folder.

B combine features.py

This script combines all the features into one file.

C PCA.py

This script conducts principal component analysis on the extract features.

D kmeans clustering.py

This script applies k-means clustering to the first 200 dimensions in PCA, with the number of clusters ranging from 6, 8, to 10.

E copy image.py

For each cluster in each clustering solution, this script randomly selects 20 images and copies them to the "img cluster" folder.

F visualize grid.py

For each clustering solution, this script creates a figure that show the randomly selected 20 images in each cluster.

Study 2B

The scripts are similar to the ones in study 2A. Please make sure to change the folder path in the scripts.