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21 May 2015 Semantically enabled image similarity search
May V. Casterline, Timothy Emerick, Kolia Sadeghi, C. Alec Gosse, Brent Bartlett, Jason Casey
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Proceedings Volume 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V; 94730I (2015) https://doi.org/10.1117/12.2177409
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States
Abstract
Georeferenced data of various modalities are increasingly available for intelligence and commercial use, however effectively exploiting these sources demands a unified data space capable of capturing the unique contribution of each input. This work presents a suite of software tools for representing geospatial vector data and overhead imagery in a shared high-dimension vector or embedding" space that supports fused learning and similarity search across dissimilar modalities. While the approach is suitable for fusing arbitrary input types, including free text, the present work exploits the obvious but computationally difficult relationship between GIS and overhead imagery. GIS is comprised of temporally-smoothed but information-limited content of a GIS, while overhead imagery provides an information-rich but temporally-limited perspective. This processing framework includes some important extensions of concepts in literature but, more critically, presents a means to accomplish them as a unified framework at scale on commodity cloud architectures.
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May V. Casterline, Timothy Emerick, Kolia Sadeghi, C. Alec Gosse, Brent Bartlett, and Jason Casey "Semantically enabled image similarity search", Proc. SPIE 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V, 94730I (21 May 2015); https://doi.org/10.1117/12.2177409
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KEYWORDS
Geographic information systems
Data modeling
Feature extraction
Image segmentation
Associative arrays
Image fusion
Image processing
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