Year: 2026
Type: article
Abstract: 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.
Institution: University of Lübeck
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W7225795219