Fancy GNG: localized color augmentation via Growing Neural Gas

Deep artificial neural networks often benefit from large and diverse training datasets. As collecting sufficiently varied data can be costly, data augmentation has become a standard component of modern computer vision pipelines. While geometric augmentations are extensively studied, most existing color augmentation methods apply global photometric transformations that primarily simulate illumination changes or camera-related effects. However, in many real-world recognition tasks, chromatic variability can be heterogeneous within an image and may differ across object or material regions rather than follow a single globally uniform photometric shift. In this work, we introduce Fancy Growing Neural Gas ( Fancy GNG ), a novel cluster-specific color augmentation method that combines Growing Neural Gas clustering with PCA-based color perturbations. Instead of applying a single global transformation to the entire image, Fancy GNG partitions the image-specific RGB distribution into coherent color-space clusters and applies independently sampled, cluster-specific perturbations. This enables structured and image-adaptive chromatic variation without relying on semantic labels or explicit spatial segmentation, while preserving the overall image content. We evaluated the proposed method on the Flowers-102, CIFAR-10, and ImageNet-1k datasets using a ResNet-50 architecture. The results reveal a strong dataset-dependent behavior. Fancy GNG achieves its largest improvements on Flowers-102, a fine-grained and highly color-sensitive classification benchmark, while also providing consistent gains on ImageNet-1k. In contrast, only marginal improvements are observed on CIFAR-10, where chromatic information appears less critical for class discrimination. Furthermore, we show that the performance degradation caused by grayscale conversion serves as a practical indicator of how strongly a dataset is likely to benefit from color-aware augmentation. Overall, the results demonstrate that cluster-specific, color-distribution-aware augmentation can provide meaningful advantages over conventional global color perturbation methods, particularly for recognition tasks in which chromatic information is both discriminative and internally structured.