I'm implementing an automatic color cast removal based on [1], which seem like a robust, simple and yet well-performing method. To avoid removing an intrinsic cast by a predominant color such as large regions of vegetation, or water, they use a method of image annotation described by [2].
The color cast detector use a multiclass support vector machine to classify image regions as sky, skin, vegetation, water or other. My problem is that [2] only describes the method, they do not include the parameters of the hyperplanes resulting from training the SVM. Training a new SVM is way out of my scope, but I haven't found any similar works including ready-to-use data. I would really appreciate one of the following:
A. A set of hyperplane parameters resulting from training using the method in [2].
B. Some other image annotation method for sky/skin/vegetation/water, including trained parameters or not requiring training.
C. Some free image database containing annotated regions of sky/skin/vegetation/water, that I can use to train a new SVM using the method in [2].
References
- F. Gasparini and R. Schettini "Color Balancing of Digital Photos Using Simple Image Statistics"
- C. Cusano, G. Ciocca and R. Schettini "Image annotation using SVM"