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Region-based image retrieval with high-level semantics using decision tree learning

8 years 4 months ago
Region-based image retrieval with high-level semantics using decision tree learning
Semantic-based image retrieval has attracted great interest in recent years. This paper proposes a region-based image retrieval system with high-level semantic learning. The key features of the system are: (1) it supports both query by keyword and query by region of interest. The system segments an image into different regions and extracts low-level features of each region. From these features, high-level concepts are obtained using a proposed decision tree-based learning algorithm named DT-ST. During retrieval, a set of images whose semantic concept matches the query is returned. Experiments on a standard real-world image database confirm that the proposed system significantly improves the retrieval performance, compared with a conventional content-based image retrieval system. (2) The proposed decision tree induction method DT-ST for image semantic learning is different from other decision tree induction algorithms in that it makes use of the semantic templates to discretize continu...
Ying Liu, Dengsheng Zhang, Guojun Lu
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2008
Where PR
Authors Ying Liu, Dengsheng Zhang, Guojun Lu
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