Combining Attention Model with Hierarchical Graph Representation for Region-Based Image Retrieval
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概要
- 論文の詳細を見る
The manifold-ranking algorithm has been successfully adopted in content-based image retrieval (CBIR) in recent years. However, while the global low-level features are widely utilized in current systems, region-based features have received little attention. In this paper, a novel attention-driven transductive framework based on a hierarchical graph representation is proposed for region-based image retrieval (RBIR). This approach can be characterized by two key properties: (1) Since the issue about region significance is the key problem in region-based retrieval, a visual attention model is chosen here to measure the regions significance. (2) A hierarchical graph representation which combines region-level with image-level similarities is utilized for the manifold-ranking method. A novel propagation energy function is defined which takes both low-level visual features and regional significance into consideration. Experimental results demonstrate that the proposed approach shows the satisfactory retrieval performance compared to the global-based and the block-based manifold-ranking methods.
- （社）電子情報通信学会の論文
- 2008-08-01
著者
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XU De
Institute of Computer Science and Engineering, Beijing Jiaotong University
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LI Bing
Institute of Computer Science and Engineering, Beijing Jiaotong University
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Li Bing
Beijing Jiaotong Univ. Beijing Chn
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Li Bing
Institute Of Computer Science And Engineering Beijing Jiaotong University
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FENG Song-He
Institute of Computer & Engineering, Beijing Jiaotong University
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Xu De
Beijing Jiaotong Univ. Beijing Chn
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Xu De
Institute Of Computer & Engineering Beijing Jiaotong University
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Feng Song-he
Institute Of Computer & Engineering Beijing Jiaotong University
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FENG Song-He
Institute of Computer & Engineering, Beijing Jiaotong University
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