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.

翻译：次优的色彩表示常常阻碍图像分割的准确性，然而许多现代算法忽视了这一关键的预处理步骤。本文提出了一种新颖的多维非线性判别分析算法——色彩空间判别分析（CSDA），以改进分割效果。该算法将线性判别分析扩展至深度学习框架，通过最大化多维有符号的类间可分性，并借助广义判别损失最小化类内变异性，从而定制色彩表示。为确保训练的稳定性，我们引入了三种替代损失函数，使得判别色彩空间与分割过程能够进行端到端的联合优化。在风力涡轮机叶片数据上的实验显示出显著的精度提升，凸显了在特定领域分割任务中定制化预处理的重要性。