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Hyperparameter-Analysis-in-Online-Constrained-Clustering

This contains MATLAB code for analysing different combinations of parameters in online constrained clustering. These are the proportion of constraints, initial number of clusters, and the batch window size.

Usage

MainIterator.m is the main file from which all the functions are called. Edit the variables to incorporate your data and your desired hyperparameters.

Data

The data must be in .csv format and stored using the following formatting:

Bounding Box (BB) Frame Data:

BB top-left x BB top-left y BB width BB height frame number frame width frame height object label frame image name
354 259 276 247 1 952 540 3 Florence_frame_00001.jpg
342 408 140 132 1 952 540 5 JP_frame_00001.jpg
218 173 371 136 1 952 540 6 Jack_frame_00001.jpg
352 72 367 240 1 952 540 8 Selwyn_frame_00001.jpg
362 276 260 228 2 952 540 3 Florence_frame_00002.jpg
341 415 129 123 2 952 540 5 JP_frame_00002.jpg

Feature Data - see link below for examples 👇

Constraints Data:

Object A Object B ML/CL
1 5 0.84631511
2 6 0.884163644
3 7 0.754439876
8 9 -1
8 10 -1

where the decimal is the IoU between the two BB's (Must-link), and '-1' means both objects are in the same frame (Cannot-link).

Examples of the datasets as used in the code can be found here 👉 https://zenodo.org/record/7322821 .

Methods

  • kul.m - Kulshreshtha method 1
  • online_cop_kmeans.m - Online COP-Kmeans
  • osl.m - Online SIngle Linkage
  • bla.m - BaseLine A "same"
  • blb.m - BaseLine B "different"
  • blc.m - BaseLine C "random

Metrics

  • normalised_mutual_information.m - Normalised Mutual Information (NMI) 2
  • adjusted_rand_index.m - Adjusted Rand Index (ARI) 2
  • classification_accuracy.m - Classification Accuracy "Count"
  • classification_accuracy_old.m - Classification Accuracy "Hungarian"

References

Footnotes

  1. Kulshreshtha, Prakhar, and Tanaya Guha. "An online algorithm for constrained face clustering in videos." 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. ↩

  2. Vinh, Nguyen Xuan, Julien Epps, and James Bailey. "Information theoretic measures for clusterings comparison: is a correction for chance necessary?." Proceedings of the 26th annual international conference on machine learning. 2009. ↩ ↩2