1,180 research outputs found
Color Constancy Using CNNs
In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling, one fully connected layer and three output nodes. Within the network
structure, feature learning and regression are integrated into one optimization
process, which leads to a more effective model for estimating scene
illumination. This approach achieves state-of-the-art performance on a standard
dataset of RAW images. Preliminary experiments on images with spatially varying
illumination demonstrate the stability of the local illuminant estimation
ability of our CNN.Comment: Accepted at DeepVision: Deep Learning in Computer Vision 2015 (CVPR
2015 workshop
Color Constancy Convolutional Autoencoder
In this paper, we study the importance of pre-training for the generalization
capability in the color constancy problem. We propose two novel approaches
based on convolutional autoencoders: an unsupervised pre-training algorithm
using a fine-tuned encoder and a semi-supervised pre-training algorithm using a
novel composite-loss function. This enables us to solve the data scarcity
problem and achieve competitive, to the state-of-the-art, results while
requiring much fewer parameters on ColorChecker RECommended dataset. We further
study the over-fitting phenomenon on the recently introduced version of
INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both
field and non-field scenes acquired by three different camera models.Comment: 6 pages, 1 figure, 3 table
- …