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Multi-feature query language for image classification

By Raoul Pascal Pein and Joan Lu


Despite the major effort put into the creation of Content-Based Image Retrieval (CBIR) system\ud during the last decade, the solutions available are still not satisfying for generic purposes.\ud The most severe issue seems to be the so-called “semantic gap”. It is feasible to define and use\ud domain specific feature vectors on a low level and use this information for a similarity based retrieval.\ud Yet, mapping these to higher level semantics remains dicult. This research investigates\ud a domain-independent way of automatized image categorization. A CBIR query language is constructed\ud to build query-like descriptors for each category to be learned. The proposed learning\ud algorithm is based on decision-trees. The resulting descriptors are aimed to be understandable\ud and modifiable by expert users. A case-study is presented to support these claims

Topics: QA75
Publisher: Elsevier
Year: 2010
OAI identifier: oai:eprints.hud.ac.uk:7671
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