Probabilistic Index Maps for Modeling Natural Signals

One of the major problems in modeling natural signals is that signals with very similar structure may locally have completely different measurements, e.g., images taken under different illumination conditions, or the speech signal captured in different environments. 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 signal representation is a representational deficiency of intensity-derived local measurements that are the basis of most efficient models. We argue that interesting structure in signals is better captured when the signal is defined as a matrix whose entries are discrete indices to a separate palette of possible measurements. In order to model the variability in signal structure, we define a signal class not by a single index map, but by a probability distribution over the index maps, which can be estimated from the data, and which we call probabilistic index maps....

• Type: Conference
• Year: 2004
• Where: UAI
• Authors: Nebojsa Jojic, Yaron Caspi, Manuel Reyes-Gomez
• Keywords: Index Map, Index Map Representation, Probabilistic Index Map, UAI 2004, UAI 2008