An Overview: Low-level Feature Fusion in Content-based Image Retrieval
底层内容特征的融合在图像检索中的研究进展

In previous content-based image retrieval algorithms,the most prevalent and convenient method in representing images is to extract low-level content features such as color,texture,shape or spatial information.But using only one low-level feature independently ignores the relevancy and coherence between features will cause a limitation on making the most of information contained in an image.The usage of single feature also confines the ability of multiple features to cooperatively illustrate images.Fusion of two or more low-level features will make a connection between features and enhance the efficiency and accuracy of image representation.Feature fusion is a trend of research in content-based image retrieval.In this paper,an up-to-date overview of low-level feature fusion algorithms is presented.In addition,a classification system of fusion algorithms is established based on the fusion levels and the content of fusion.The existing problems and open questions in this field are also indicated.