# ColorNet: Investigating the importance of color spaces for image classification

arXiv: https://arxiv.org/abs/1902.00267 · HTML: https://mlsanity.com/paper/1902.00267/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 30 of 31)

- [MedMamba: Vision Mamba for Medical Image Classification](https://mlsanity.com/paper/2403.03849/) (2024)
- [RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization](https://mlsanity.com/paper/2206.12694/) (2022)
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- [CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation](https://mlsanity.com/paper/2408.00181/) (2024)
- [From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications](https://mlsanity.com/paper/2308.16041/) (2023)
- [Astroformer: More Data Might not be all you need for Classification](https://mlsanity.com/paper/2304.05350/) (2023)
- [MultiColor: Image Colorization by Learning from Multiple Color Spaces](https://mlsanity.com/paper/2408.04172/) (2024)
- [Continual Learning Improves Zero-Shot Action Recognition](https://mlsanity.com/paper/2410.10497/) (2024)
- [Low-Effort Jailbreak Attacks Against Text-to-Image Safety Filters](https://mlsanity.com/paper/2604.01888/) (2026)
- [Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet](https://mlsanity.com/paper/2110.09428/) (2021)
- [Stroke-based Rendering: From Heuristics to Deep Learning](https://mlsanity.com/paper/2302.00595/) (2022)
- [Telling Stories for Common Sense Zero-Shot Action Recognition](https://mlsanity.com/paper/2309.17327/) (2023)
- [FE-Adapter: Adapting Image-based Emotion Classifiers to Videos](https://mlsanity.com/paper/2408.02421/) (2024)
- [Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution](https://mlsanity.com/paper/2406.12623/) (2024)
- [Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring](https://mlsanity.com/paper/2407.16264/) (2024)
- [Improvement of Spiking Neural Network with Bit Planes and Color Models](https://mlsanity.com/paper/2410.08229/) (2024)
- [An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging](https://mlsanity.com/paper/2410.03359/) (2024)
- [ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces](https://mlsanity.com/paper/2503.05247/) (2025)
- [HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population](https://mlsanity.com/paper/2108.01411/) (2021)
- [Galaxy Classification Using Transfer Learning and Ensemble of CNNs With Multiple Colour Spaces](https://mlsanity.com/paper/2305.00002/) (2023)
- [What's color got to do with it? Face recognition in grayscale](https://mlsanity.com/paper/2309.05180/) (2023)
- [A Novel Approach to Image Steganography Using Generative Adversarial Networks](https://mlsanity.com/paper/2412.00094/) (2024)
- [Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization](https://mlsanity.com/paper/2503.00228/) (2025)
- [Development and evaluation of a deep learning algorithm for German word recognition from lip movements](https://mlsanity.com/paper/2504.15792/) (2025)
- [Distribution-Based Masked Medical Vision-Language Model Using Structured Reports](https://mlsanity.com/paper/2507.21794/) (2025)
- [Multivariate Planar Curves: A Statistical Framework for Shape Analysis in Images](https://mlsanity.com/paper/2508.11780/) (2025)
- [Discriminant Learning-based Colorspace for Blade Segmentation](https://mlsanity.com/paper/2601.13816/) (2026)
- [Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques](https://mlsanity.com/paper/2401.17883/) (2024)
- [A Study on the Effect of Color Spaces in Learned Image Compression](https://mlsanity.com/paper/2406.13709/) (2024)

## Abstract (arXiv)

Image classification is a fundamental application in computer vision. Recently, deeper networks and highly connected networks have shown state of the art performance for image classification tasks. Most datasets these days consist of a finite number of color images. These color images are taken as input in the form of RGB images and classification is done without modifying them. We explore the importance of color spaces and show that color spaces (essentially transformations of original RGB images) can significantly affect classification accuracy. Further, we show that certain classes of images are better represented in particular color spaces and for a dataset with a highly varying number of classes such as CIFAR and Imagenet, using a model that considers multiple color spaces within the same model gives excellent levels of accuracy. Also, we show that such a model, where the input is preprocessed into multiple color spaces simultaneously, needs far fewer parameters to obtain high accuracy for classification. For example, our model with 1.75M parameters significantly outperforms DenseNet 100-12 that has 12M parameters and gives results comparable to Densenet-BC-190-40 that has 25.6M parameters for classification of four competitive image classification datasets namely: CIFAR-10, CIFAR-100, SVHN and Imagenet. Our model essentially takes an RGB image as input, simultaneously converts the image into 7 different color spaces and uses these as inputs to individual densenets. We use small and wide densenets to reduce computation overhead and number of hyperparameters required. We obtain significant improvement on current state of the art results on these datasets as well.