**Martin Jägersand
Department of Computer Science, University of Rochester, Rochester, NY 14627
http://www.cs.rochester.edu/u/jag/**

Information measures with respect to spatial locations and scales of objects in an image are important to image processing and interpretation. It allows us to focus , saving effort and reducing false positives. In particular, the information content of a man-made scene is typically confined to a small set of scales. We devise a scale space based measure of image information. Kullback contrasts between successive resolution lengths gives the differential information gain. Experiments show that this measure gives a clear indication of characteristic lengths in a variety of real world images and is superior to power spectrum based measurements. Decomposing the expected information gain into spatial coordinates gives us a saliency map for use by an attention selector. Last we combine the scale and spatial decompositions into a single information measure, giving both the spatial extent and scale range of interest. The information measure has an efficient implementation, and thus can be used routinely in early vision processing.

- 1 Introduction
- 2 Theory
- 3 Experimental Results
- 4 Comparison to Other Measures
- 5 Spatial decomposition by patch-wise computation
- 6 A Different Measure: ``Lininess''
- 7 An Information Infinitesimal Decomposed in Both Scale and Spatial Coordinates
- 8 Implementation
- 9 Discussion and future work
- References