现有的视觉词典构建方法一般是将多个特征构成整个向量并通过聚类形成视觉词典，这种方法在视觉聚类的过程中只考虑了特征的整体相似性而忽略了不同特征对构建视觉词典的影响。提出了一种基于Dempster-Shafer（D-S）证据理论的多特征融合的视觉词典构建方法。该方法应用证据理论融合不同特征的视觉相似性，构造出更加精确的视觉词典。在证据理论的基础上，使用两种特征实现了对视觉词典的再分，使得相似的特征更好地集中在一类中。与传统视觉词典构建方法相比，本文方法获得了更好的结果。应用以上视觉词典构造方法并将之应用于分类实验，在支持向量机与朴素贝叶斯分类器上取得的分类实验结果表明，应用本文方法构建的视觉词典能有效提高视觉词典的精确度，分类效果得到了很大的提高。

The existing visual dictionary construction methods need to combine several features into a vector. Then the vectors are clustered to form the dictionary. Those approaches only take the similarity of all the features into consideration but the neglect distinct roles of diverse features on the construction of the visual dictionary. In this paper, a visual dictionary construction method based on Dempster-Shafer (D-S) evidence theory is proposed. D-S evidence theory is applied to fuse different features in their similarities, which is helpful to obtain more accurate visual dictionaries. Two kinds of features are applied in this paper to subdivide the initial visual dictionary based on the Dempster-Shafer evidence theory, and similar features are clustered together better. Compared with the traditional visual dictionary generation method, our proposed method obtains better results. The experimental results on image classification using support vector machine (SVM) and Naive Bayisan (NB) classifiers show that our proposed method outperforms the K-Means based dictionary construction algorithm in terms of accuracy in visual dictionary and image classification.