Pein, Raoul Pascal and Lu, Joan (2010) Multi-feature query language for image classification. Procedia Computer Science, 1 (1). pp. 2533-2541. ISSN 1877-0509
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Despite the major effort put into the creation of Content-Based Image Retrieval (CBIR) system
during the last decade, the solutions available are still not satisfying for generic purposes.
The most severe issue seems to be the so-called “semantic gap”. It is feasible to define and use
domain specific feature vectors on a low level and use this information for a similarity based retrieval.
Yet, mapping these to higher level semantics remains dicult. This research investigates
a domain-independent way of automatized image categorization. A CBIR query language is constructed
to build query-like descriptors for each category to be learned. The proposed learning
algorithm is based on decision-trees. The resulting descriptors are aimed to be understandable
and modifiable by expert users. A case-study is presented to support these claims.
|Additional Information:||Paper presented at the International Conference on Computational Science (iccs) 2010 University of Amsterdam The Netherlands May 31 - June 2, 2010|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Schools:||School of Computing and Engineering
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
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School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
|Depositing User:||Raoul Pein|
|Date Deposited:||25 May 2010 10:38|
|Last Modified:||22 Dec 2016 16:21|
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