Object recognition via information: theoretic measures/metrics

Daniel W. Repperger; Alan R. Pinkus; Julie A. Skipper; Christina D. Schrider

doi:10.1117/12.702504

28 February 2007 Object recognition via information: theoretic measures/metrics

Daniel W. Repperger, Alan R. Pinkus, Julie A. Skipper, Christina D. Schrider

Proceedings Volume 6498, Computational Imaging V; 64980Z (2007) https://doi.org/10.1117/12.702504
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

Discrimination of friendly or hostile objects is investigated using information-theory measures/metric in an image which has been compromised by a number of factors. In aerial military images, objects with different orientations can be reasonably approximated by a single identification signature consisting of the average histogram of the object under rotations. Three different information-theoretic measures/metrics are studied as possible criteria to help classify the objects. The first measure is the standard mutual information (MI) between the sampled object and the library object signatures. A second measure is based on information efficiency, which differs from MI. Finally an information distance metric is employed which determines the distance, in an information sense, between the sampled object and the library object. It is shown that the three (parsimonious) information-theoretic variables introduced here form an independent basis in the sense that any variable in the information channel can be uniquely expressed in terms of the three parameters introduced here. The methodology discussed is tested on a sample set of standardized images to evaluate their efficacy. A performance standardization methodology is presented which is based on manipulation of contrast, brightness, and size attributes of the sample objects of interest.

Citation Download Citation

Daniel W. Repperger, Alan R. Pinkus, Julie A. Skipper, and Christina D. Schrider "Object recognition via information: theoretic measures/metrics", Proc. SPIE 6498, Computational Imaging V, 64980Z (28 February 2007); https://doi.org/10.1117/12.702504

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