) Initial query. The tag query method and the semantic query method will be used. The initial query results includes the results searched by both the methods. (2) Reorganization of the initial query results. The repeated images will be cleared and only one image will be left. (3) Distance based results rechecked. The most relative image will be ranked in front of the list and the less correlation image will be cleared. (4) Reordering the images.All the images will be reordered by the distance which reflects the correlation of the query image. According to the experimental verification, the combined method can improve the query precision.

Art image databases play an important role in the image research history. Art image can be classified into various categories, and each category has its own characteristics. Art image retrieval with high precision and speed can help the researchers understand an art image much more easily. In addition, the classification for the art images can help the researchers improve the working efficiency. In the art image retrieval, the query precision is much more important than the query speed. Improving the query precision at the cost of not seriously decreasing the query speed can be accepted. In this paper, a new method has been proposed to improve the query precision. The new method mainly includes initial query, reorganization, results recheck and images reordering. At the beginning, the tag query method and the semantic query method will be used to search the initial image results; then, the results will be reorganization according to the semantic method; finally, the images in the results will be filtered by the incomplete filtration method. According to the experimental results, the new method is proved that it can improve the query precision. The new method can be used in the art image retrieval process.