Abstract
The traditional content-based image retrieval system can not meet the users’ needs due to the ‘semantic gap’ between low-level features and the richness of human semantics. In an attempt to narrow the ‘semantic gap’, an approach which combines region-based image retrieval (RBIR) and relevance feedback (RF) with semantic color and spatial location features is presented in this paper. This method segments images into homogeneous regions and gives name for each region. In addition, the importance of each region is also taken into account. The Earth Mover’s Distance (EMD) is employed to measure the image similarity. To further improve the efficiency and accuracy of retrieval system, relevance feedback techniques are introduced in this paper. Experimental results on 1000 images database demonstrate the effectiveness and the efficiency of our approach is encouraging.
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