DocumentCode :
1940613
Title :
Incorporating Domain High-Level Concepts into Heuristic Searches: A Case Study on Identifying Plant Species in Remote Sensing Images
Author :
Zhou, J.H. ; Zhou, Y.F.
Author_Institution :
China Educ. Minist. Key Lab. of Geogr. Inf. Sci., East China Normal Univ., Shanghai, China
fYear :
2011
fDate :
5-7 Aug. 2011
Firstpage :
632
Lastpage :
636
Abstract :
In order to take domain expert knowledge as a supplement of the machine "intelligence", a High-Level-Concept-based framework (HLC) for heuristic search is proposed. HLC has domain experts actively generalize their own psychological feelings about image features into high-level concepts, then write down them in descriptors, and make the domain high-level concepts enter a heuristic search with a multi-descriptor combination. These so-called "descriptors" are a kind of non-threshold models so that they have reusability and generalization. To demonstrate the idea, 18 new descriptor have been designed. The examinations of discrimination accuracy indicate that in a multi-descriptor space, the error rates of classification is 67.91% lower than that in a spectral-brightness space.
Keywords :
expert systems; geophysical image processing; image classification; knowledge engineering; remote sensing; vegetation; descriptors; domain expert knowledge; domain high level concepts; heuristic searches; high level concept based framework; nonthreshold models; plant species identification; remote sensing images; spectral-brightness space; Accuracy; Educational institutions; Error analysis; Machine learning; Psychology; Remote sensing; Vegetation mapping; descriptors; domain high-level concepts; machine discrimination; plant species;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2011 Second International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-1-4577-0755-1
Electronic_ISBN :
978-0-7695-4455-7
Type :
conf
DOI :
10.1109/ICDMA.2011.157
Filename :
6051926
Link To Document :
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