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Abstract. This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high d...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.114.9778 2023-05-15T15:05:57+02:00 Observation The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.9778 http://ase.arc.nasa.gov/publications/pdf/0674.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.9778 http://ase.arc.nasa.gov/publications/pdf/0674.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://ase.arc.nasa.gov/publications/pdf/0674.pdf Mercer Kernels Image Understanding Image Analysis text ftciteseerx 2016-01-07T13:50:57Z Abstract. This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences. Text Arctic Unknown Arctic Mercer ENVELOPE(65.647,65.647,-70.227,-70.227) |
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Mercer Kernels Image Understanding Image Analysis |
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Mercer Kernels Image Understanding Image Analysis Observation |
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Mercer Kernels Image Understanding Image Analysis |
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Abstract. This paper presents a new methodology for automatic knowledge driven image mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. In that high dimensional feature space, linear clustering, prediction, and classification algorithms can be applied and the results can be mapped back down to the original image space. Thus, highly nonlinear structure in the image can be recovered through the use of well-known linear mathematics in the feature space. This process has a number of advantages over traditional methods in that it allows for nonlinear interactions to be modelled with only a marginal increase in computational costs. In this paper, we present the theory of Mercer Kernels, describe its use in image mining, discuss a new method to generate Mercer Kernels directly from data, and compare the results with existing algorithms on data from the MODIS (Moderate Resolution Spectral Radiometer) instrument taken over the Arctic region. We also discuss the potential application of these methods on the Intelligent Archive, a NASA initiative for developing a tagged image data warehouse for the Earth Sciences. |
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The Pennsylvania State University CiteSeerX Archives |
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Text |
title |
Observation |
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Observation |
title_full |
Observation |
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Observation |
title_full_unstemmed |
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observation |
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.9778 http://ase.arc.nasa.gov/publications/pdf/0674.pdf |
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ENVELOPE(65.647,65.647,-70.227,-70.227) |
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Arctic Mercer |
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Arctic Mercer |
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Arctic |
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Arctic |
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http://ase.arc.nasa.gov/publications/pdf/0674.pdf |
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.9778 http://ase.arc.nasa.gov/publications/pdf/0674.pdf |
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Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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