Improving an Adaptive Image Interpretation System by Leveraging
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CONTRIBUTORS:
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CONFERENCE TITLE:
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CONF. LOCATION:
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None
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YEAR:
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2003
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PUB TYPE:
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Conference Paper
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SUBJECT(S):
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adaptive image interpretation system, leveraging for regression, boosting, sequential decision making.
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DISCIPLINE:
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Computer Science
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HTTP:
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LANGUAGE:
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English
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PUB ID:
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103-397-072
(Last edited on
2003/11/21 21:09:42 US/Mountain)
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SPONSOR(S):
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ABSTRACT:
Automated image interpretation is an important task in numerous applications ranging from security systems to natural resource inventorization based on remote-sensing.
Recently, a second generation of adaptive machine-learned
image interpretation system (ADORE) has shown expertlevel
performance in several challenging domains. Its extension,
MR ADORE, aims at removing the last vestiges of human intervention still present in the original design of ADORE. Both systems treat the image interpretation process
as a sequential decision making process guided by a machine-learned heuristic value function. This paper employs a new leveraging algorithm for regression (RESLEV)
to improve the learnability of the heuristics in MR ADORE.
Experiments show that RESLEV improves the system’s performance if the base learners are weak. Further analysis
discovers the difference between regression and decision-making problems, and suggests an interesting research direction.
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