  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.
