In view of application requirements of visual dictionary in image representation and retrieval, this paper proposes an image retrieval method based on the combination of multiple visual dictionaries and saliency weight, which can represent image features with saliency and sparsity. Firstly, the image is divided into blocks, and different kinds of underlying features of image blocks are extracted. Secondly, the image block features are used to learn the multiple visual dictionaries through non-negative sparse coding. The spatial information and saliency are introduced into the sparse vectors for the image blocks by the saliency pooling method, and saliency weight is introduced to form the sparse representation of the entire image. Finally, a proposed SDD distance is used for image retrieval. Compared with the method of single visual dictionary on common image dataset Corel and Caltech, Experimental results demonstrate that the proposed method can effectively improve the image retrieval accuracy.