- Title
- Region-based image retrieval with high-level semantics using decision tree learning
- Creator
- Liu, Ying; Zhang, Dengsheng; Lu, Guojun
- Date
- 2008
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/73556
- Identifier
- vital:7090
- Identifier
- ISSN:0031-3203
- Abstract
- 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 continuous-valued region features and avoids the difficult image feature discretization problem. Furthermore, it introduces a hybrid tree simplification method to handle the noise and tree fragmentation problems, thereby improving the classification performance of the tree. Experimental results indicate that DT-ST outperforms two well-established decision tree induction algorithms ID3 and C4.5 in image semantic learning.
- Relation
- Pattern Recognition Vol. 41, no. 8 (2008), p. 2554-2570
- Rights
- Copyright Pergamon
- Rights
- This metadata is freely available under a CCO license
- Subject
- RBIR; Semantic image retrieval; Decision tree learning; Semantic template; CBIR
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