在基于内容的图像检索中，提取颜色、纹理、形状或空间信息等底层特征是目前最常用且简便的表征图像的方法。但使用单一底层特征容易忽视特征间的相互联系，无法对图像以各种形式提供的信息加以充分利用，限制了众多特征联合诠释图像的可能性。底层内容特征的融合可以全面同时互补地表示图像中包含的各类信息，有效地利用特征间的联系，提高了图像内容表示的效率和精度。该文对现有的底层内容的融合特征提取算法进行总结，提出了一种以融合的层次及融合内容为依据的分类体系，指出了基于融合特征的图像检索现今存在的问题以及一些可能的研究方向。

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.