Learning Outdoor Color Classification from Just One Training Image

We present an algorithm for color classification with explicit illuminant estimation and compensation. A Gaussian classifier is trained with color samples from just one training image. Then, using a simple diagonal illumination model, the illuminants in a new scene that contains some of the same surface classes are estimated in a Maximum Likelihood framework using the Expectation Maximization algorithm. We also show how to impose priors on the illuminants, effectively computing a Maximum-A-Posteriori estimation. Experimental results show the excellent performances of our classification algorithm for outdoor images. 1

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Roberto Manduchi
• Keywords: Classification Algorithm, Computer Vision, ECCV 2004, Expectation Maximization Algorithm, Explicit Illuminant Estimation, Maximum Likelihood Framework, Simple Diagonal Illumination