Capturing Image Structure with Probabilistic Index Maps

One of the major problems in modeling images for vision tasks is that images with very similar structure may locally have completely different appearance, e.g., images taken under different illumination conditions, or the images of pedestrians with different clothing. While there have been many successful attempts to address these problems in application-specific settings, we believe that underlying a large set of problems in vision is a representational deficiency of intensity-derived local measurements that are the basis of most efficient models. We argue that interesting structure in images is better captured when the image is defined as a matrix whose entries are discrete indices to a separate palette of possible intensities, colors or other features, much like the image representation often used to save on storage. In order to model the variability in images, we define an image class not by a single index map, but by a probability distribution over the index maps, which can be au...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Nebojsa Jojic, Yaron Caspi
• Keywords: Computer Vision, CVPR 2004, Image Representation, Index Map Representation, Probabilistic Index Maps, Separate Palette, Single Index Map