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Proceedings Paper

Toward interactive search in remote sensing imagery
Author(s): Reid Porter; Don Hush; Neal Harvey; James Theiler
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Paper Abstract

To move from data to information in almost all science and defense applications requires a human-in-the-loop to validate information products, resolve inconsistencies, and account for incomplete and potentially deceptive sources of information. This is a key motivation for visual analytics which aims to develop techniques that complement and empower human users. By contrast, the vast majority of algorithms developed in machine learning aim to replace human users in data exploitation. In this paper we describe a recently introduced machine learning problem, called rare category detection, which may be a better match to visual analytic environments. We describe a new design criteria for this problem, and present comparisons to existing techniques with both synthetic and real-world datasets. We conclude by describing an application in broad-area search of remote sensing imagery.

Paper Details

Date Published: 28 April 2010
PDF: 10 pages
Proc. SPIE 7709, Cyber Security, Situation Management, and Impact Assessment II; and Visual Analytics for Homeland Defense and Security II, 77090V (28 April 2010); doi: 10.1117/12.850787
Show Author Affiliations
Reid Porter, Los Alamos National Lab. (United States)
Don Hush, Los Alamos National Lab. (United States)
Neal Harvey, Los Alamos National Lab. (United States)
James Theiler, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 7709:
Cyber Security, Situation Management, and Impact Assessment II; and Visual Analytics for Homeland Defense and Security II
William J. Tolone; John F. Buford; William Ribarsky; Gabriel Jakobson; John Erickson, Editor(s)

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