Discriminant Learning-based Colorspace for Blade Segmentation
This work addresses the critical preprocessing step of color representation for domain-specific segmentation, particularly in wind turbine blade analysis, but it is incremental as it extends Linear Discriminant Analysis into a deep learning context.
The paper tackled the problem of suboptimal color representation hindering accurate image segmentation by introducing Colorspace Discriminant Analysis (CSDA), a novel multidimensional nonlinear discriminant analysis algorithm that customizes color representation to maximize inter-class separability and minimize intra-class variability, resulting in significant accuracy gains on wind turbine blade data.
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.