提出一种基于互信息约束聚类的图像标注算法。采用语义约束对信息瓶颈算法进行改进，并用改进的信息瓶颈算法对分割后的图像区域进行聚类，建立图像语义概念和聚类区域之间的相互关系；对未标注的图像，提出一种计算语义概念的条件概率的方法，同时考虑训练图像的先验知识和区域的低层特征，最后使用条件概率最大的语义关键字对图像区域语义自动标注。对一个包含500幅图像的图像库进行实验，结果表明，该方法比其他方法更有效。

An image annotation method based on mutual information and constrained clustering is proposed. We utilized the semantic constraint to improve information bottleneck method,which employed to cluster the segmented region.Then relationships between image semantic concept and clustering regions are established. Toward the un-annotated image, a new method is proposed to calculate the conditional probability of each semantic concept,while considering the prior knowledge of training images and low-level features of the segmented regions.Finally,the image region semantics are automatically annotated by keywords with maximal conditional probability. The proposed method has been implemented and tested on an image database with about 500 images. The experimental results show that the effectiveness of the proposed method outperforms other approaches.