Content-based Color Image Retrieval Using Multi-semantics
Hongying Yang · 2009
The performance of content-based image retrieval (CBIR) systems is largely limited by the between the low-level feature and high-level concept.A new content-based color image retrieval method using multi-semantics was proposed,which not only takes into consideration the important image edge information and human visual system,but also utilizes the support vector regression (SVR) theory.Firstly,the important image edge was extracted by using canny detection operator and the low-level texture feature was computed.Then,the high-level texture was determined by mapping the low-level feature using SVR.Secondly,the high-level color could be obtained by using the main color of the most important region.Finally,image retrieval was implemented by using both texture and color semantic.Experimental results show that the proposed image retrieval is effective in characterizing image high-level and can provide sound and robust image retrieval performance,which has strong significance for reducing the semantic gap between the visual feature and concept.