基于Tsetlin Machine复合结构的先进图像处理优化工具箱
An Optimized Toolbox for Advanced Image Processing with Tsetlin Machine Composites
AI总结：
本文提出基于TM Composites架构并融合多种图像处理技术的TM Specialist工具箱，通过严格超参数搜索在CIFAR-10上实现82.8%的TM新最优准确率，为TM图像分析应用奠定基础。
摘要：
Tsetlin Machine（TM）已在多个图像分类基准上取得了具有竞争力的结果，包括MNIST、K-MNIST、F-MNIST和CIFAR-2。然而，对于TM而言，彩色图像分类可以说仍处于起步阶段，CIFAR-10一直是跟踪进展的焦点。在过去几年中，随着Drop Clause的引入，TM在CIFAR-10上的准确率已从2020年的约61%提高到2023年的75.1%。在本文中，我们利用最近提出的TM Composites架构，并引入了一系列使用各种图像处理技术的TM Specialist。这些技术包括Canny边缘检测、方向梯度直方图、自适应均值阈值化、自适应高斯阈值化、Otsu阈值化、颜色温度计以及自适应颜色温度计。此外，我们进行了严格的超参数搜索，在其中为若干TM Specialist发现了最优超参数。结果是一个工具箱，为TM在CIFAR-10上提供了新的最先进结果，准确率达到82.8%。总之，我们的TM Specialist工具箱为新的TM应用奠定了基础，并成为进一步研究图像分析中TM Composites的一个里程碑。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
