# An Optimized Toolbox for Advanced Image Processing with Tsetlin Machine Composites

arXiv: https://arxiv.org/abs/2406.00704 · HTML: https://mlsanity.com/paper/2406.00704/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 2 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic](https://mlsanity.com/paper/1804.01508/) (2018)
- [The Convolutional Tsetlin Machine](https://mlsanity.com/paper/1905.09688/) (2019)
- [Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications](https://mlsanity.com/paper/1809.04547/) (2018)
- [Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier](https://mlsanity.com/paper/2011.08755/) (2020)
- [TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines](https://mlsanity.com/paper/2309.04801/) (2023)
- [Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification](https://mlsanity.com/paper/2301.10181/) (2023)

## Built on by (most cited first, 7 of 7)

- [An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator](https://mlsanity.com/paper/2501.19347/) (2025)
- [Dynamic Tsetlin Machine Accelerators for On-Chip Training at the Edge using FPGAs](https://mlsanity.com/paper/2504.19797/) (2025)
- [The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs](https://mlsanity.com/paper/2507.14874/) (2025)
- [Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Space](https://mlsanity.com/paper/2505.16386/) (2025)
- [Uncertainty Quantification in the Tsetlin Machine](https://mlsanity.com/paper/2507.04175/) (2025)
- [A Methodology for Transparent Logic-Based Classification Using a Multi-Task Convolutional Tsetlin Machine](https://mlsanity.com/paper/2510.01906/) (2025)
- [CRISP: A Framework for Clause-Reconstructed Interpretable NeuroSymbolic Propositions](https://mlsanity.com/paper/2610.02431/) (2026)

## Abstract (arXiv)

The Tsetlin Machine (TM) has achieved competitive results on several image classification benchmarks, including MNIST, K-MNIST, F-MNIST, and CIFAR-2. However, color image classification is arguably still in its infancy for TMs, with CIFAR-10 being a focal point for tracking progress. Over the past few years, TM's CIFAR-10 accuracy has increased from around 61% in 2020 to 75.1% in 2023 with the introduction of Drop Clause. In this paper, we leverage the recently proposed TM Composites architecture and introduce a range of TM Specialists that use various image processing techniques. These include Canny edge detection, Histogram of Oriented Gradients, adaptive mean thresholding, adaptive Gaussian thresholding, Otsu's thresholding, color thermometers, and adaptive color thermometers. In addition, we conduct a rigorous hyperparameter search, where we uncover optimal hyperparameters for several of the TM Specialists. The result is a toolbox that provides new state-of-the-art results on CIFAR-10 for TMs with an accuracy of 82.8%. In conclusion, our toolbox of TM Specialists forms a foundation for new TM applications and a landmark for further research on TM Composites in image analysis.