Discriminant Learning-based Colorspace for Blade Segmentation

• URL: http://arxiv.org/abs/2601.13816v1
• Date: Tue, 20 Jan 2026 10:23:23 GMT
• Title: Discriminant Learning-based Colorspace for Blade Segmentation
• Authors: Raül Pérez-Gonzalo, Andreas Espersen, Antonio Agudo,
• Abstract summary: Suboptimal color representation often hinders accurate image segmentation, yet many modern algorithms neglect this critical preprocessing step.<n>This work presents a novel multidimensional nonlinear discriminant analysis algorithm, Colorspace Discriminant Analysis (CSDA) for improved segmentation.<n> Experiments on wind turbine blade data demonstrate significant accuracy gains, emphasizing the importance of tailored preprocessing in domain-specific segmentation.
• Score: 18.108693090007748
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Suboptimal color representation often hinders accurate image segmentation, yet many modern algorithms neglect this critical preprocessing step. This work presents a novel multidimensional nonlinear discriminant analysis algorithm, Colorspace Discriminant Analysis (CSDA), for improved segmentation. Extending Linear Discriminant Analysis into a deep learning context, CSDA customizes color representation by maximizing multidimensional signed inter-class separability while minimizing intra-class variability through a generalized discriminative loss. To ensure stable training, we introduce three alternative losses that enable end-to-end optimization of both the discriminative colorspace and segmentation process. Experiments on wind turbine blade data demonstrate significant accuracy gains, emphasizing the importance of tailored preprocessing in domain-specific segmentation.

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