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With the rapid development of digital imaging technology, image annotation is an important and challenging task in image retrieval. At present, many machine learning methods have been applied to solve the problem of automatic image annotation (AIA). However, there exists enormous semantic expressive gap between the low-level image features and high-level semantic concepts. Due to the problem, the annotation performance of existing methods is not satisfactory, and needs to be further improved. This paper proposes an automatic annotation framework via a novel decision tree-based Bayesian (DTB) machine learning algorithm. It is a hybrid approach that attempts to utilize the advantages of both DT and Naive-Bayesian (NB). We firstly segment an image into different regions and extract low-level features of each region. From these features, high-level semantic concepts are obtained using a DTB learning algorithm. Finally, experiments conducted on the Corel dataset demonstrate the effectiveness of DTB machine learning. The DTB can not only enhance the classification accuracy, but also associate low-level region features with high-level image concepts. This method presents the advantages of the Bayesian method and the DT. Moreover, this semantic interpretation capability is a natural simulation of human learning.