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

ColorNet: Investigating the importance of color spaces for image classification