Unsupervised feature learning with sparse autoencoders in YUV space

Existing unsupervised feature learning algorithms usually extract features in RGB color space, but YUV color space is widely adopted in image and video compression standards. In order to take advantage of human visual characteristics and avoid the calculation consumption caused by color space conversion, an unsupervised feature learning approach in YUV space based on sparse autoencoders is presented. First, image patches in YUV space are randomly sampled and whitened, and then are fed into sparse autoencoders to learn local features in an unsupervised way. Considering the characteristic that the luminance channel and chrominance channels are independent in YUV space, a whitening method which treats the luminance and chrominance separately is proposed in the pre-processing step. Finally, features learned over local image patches are convolved with large-size images in order to get global feature activations. Global features are then sent into image classification systems for performance testing. Experimental results reveal that unsupervised feature learning in YUV space achieves similar or even slightly better performance in color image classification compared with that in RGB space as long as the luminance component is whitened properly.