Composition analysis-based relevance ranking for ancient mural
Dongming Lu · Journal of Zhejiang University(Engineering Science) · 2012
The present image retrieval technologies have difficulties in retrieving ancient murals,since they lack of the abilities to handle complex semantic and features of layout in painting.This work puts forward a new relevance ranking model based on composition analysis to improve ancient mural retrieval.By introducing the theory of composition on painting,the relevance ranking model measures the relevance of mural images from three aspects which are layout,topic and semantics,and reduces the semantic gap between the content of mural and the real intention of the user.The relevance ranking model was seamlessly integrated into a unified framework for semantic query expansion to improve the precision of Top N results while maintaining a high recall.Experimental results of the Dunhuang Murals show that compared with the baseline method,the R-Precision ratio of semantic mural retrieval based on this model can be increased by 36% on average.