Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
William Freeman, Antonio Torralba
The goal of low-level vision is to estimate an underlying scene, given an observed image. Real-world scenes (eg, albedos or shapes) can be very complex, conventionally requiring high dimensional representations which are hard to estimate and store. We propose a low-dimensional rep- resentation, called a scene recipe, that relies on the image itself to de- scribe the complex scene conﬁgurations. Shape recipes are an example: these are the regression coefﬁcients that predict the bandpassed shape from image data. We describe the beneﬁts of this representation, and show two uses illustrating their properties: (1) we improve stereo shape estimates by learning shape recipes at low resolution and applying them at full resolution; (2) Shape recipes implicitly contain information about lighting and materials and we use them for material segmentation.