Image Analysis Through Local Information Measures

The properties of local image statistics are analyzed in a classic information theoretic setting. Local spatiochromatic image elements are projected into a space in which constituent components are independent by way of independent component analysis, allowing a fast and tractable means of considering the joint likelihood of such statistics. Observation of this likelihood allows inferences to be made regarding the informativeness of a particular set of statistics. This operation is shown to illuminate a number of perceptually important image properties, allowing figure-ground segmentation, removal of common or expected image elements, and prediction of regions of interest.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Neil Bruce
• Keywords: Computer Vision, ICPR 2004, Important Image Properties, Likelihood Allows Inferences, Local Image Statistics