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arXiv · 2601.13816

Discriminant Learning-based Colorspace for Blade Segmentation

Raül Pérez-Gonzalo·Andreas Espersen·Antonio Agudo
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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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Raül Pérez-Gonzalo, Andreas Espersen, Antonio Agudo. 2026-01-20. Discriminant Learning-based Colorspace for Blade Segmentation. https://arxiv.org/abs/2601.13816

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