Combining Attention Model with Hierarchical Graph Representation for Region-Based Image Retrieval

Song-He FENG  De XU  Bing LI  

IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.8   pp.2203-2206
Publication Date: 2008/08/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.8.2203
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
region-based image retrieval,  manifold-ranking,  hierarchical graph representation,  visual attention model,  

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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.