Summary: Recovering Intrinsic Images
from a Single Image
Marshall F. Tappen, William T. Freeman, Member, IEEE, and Edward H. Adelson, Member, IEEE
Abstract--Interpreting real-world images requires the ability distinguish the different characteristics of the scene that lead to its final
appearance. Two of the most important of these characteristics are the shading and reflectance of each point in the scene. We present
an algorithm that uses multiple cues to recover shading and reflectance intrinsic images from a single image. Using both color
information and a classifier trained to recognize gray-scale patterns, given the lighting direction, each image derivative is classified as
being caused by shading or a change in the surface's reflectance. The classifiers gather local evidence about the surface's form and
color, which is then propagated using the Generalized Belief Propagation algorithm. The propagation step disambiguates areas of the
image where the correct classification is not clear from local evidence. We use real-world images to demonstrate results and show how
each component of the system affects the results.
Index Terms--Computer vision, machine learning, reflectance, shading, boosting, belief propagation.
THE appearance of a scene depends on many character-
istics, such as the illumination of the scene, the shape of
the surfaces in the scene, and the reflectance of each surface.
Each of these characteristics contains useful information
about the objects in the scene. Barrow and Tenenbaum 
proposed using intrinsic images to represent these character-