Patch and Shuffle: A Preprocessing Technique for Texture Classification in Autonomous Cementitious Fabrication

• URL: http://arxiv.org/abs/2504.10353v1
• Date: Mon, 14 Apr 2025 16:03:21 GMT
• Title: Patch and Shuffle: A Preprocessing Technique for Texture Classification in Autonomous Cementitious Fabrication
• Authors: Jeremiah Giordani,
• Abstract summary: "patch and shuffle" technique segments input images into smaller patches, shuffles them, and reconstructs a jumbled image before classification.<n>We evaluate this approach on a dataset of extruded cement images, using a ResNet-18-based architecture.<n>Results show a significant improvement in accuracy: the patch and shuffle model achieved 90.64% test accuracy versus 72.46% for the baseline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous fabrication systems are transforming construction and manufacturing, yet they remain vulnerable to print errors. Texture classification is a key component of computer vision systems that enable real-time monitoring and adjustment during cementitious fabrication. Traditional classification methods often rely on global image features, which can bias the model toward semantic content rather than low-level textures. In this paper, we introduce a novel preprocessing technique called "patch and shuffle," which segments input images into smaller patches, shuffles them, and reconstructs a jumbled image before classification. This transformation removes semantic context, forcing the classifier to rely on local texture features. We evaluate this approach on a dataset of extruded cement images, using a ResNet-18-based architecture. Our experiments compare the patch and shuffle method to a standard pipeline, holding all other factors constant. Results show a significant improvement in accuracy: the patch and shuffle model achieved 90.64% test accuracy versus 72.46% for the baseline. These findings suggest that disrupting global structure enhances performance in texture-based classification tasks. This method has implications for broader vision tasks where low-level features matter more than high-level semantics. The technique may improve classification in applications ranging from fabrication monitoring to medical imaging.

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