提出了一种基于优化Adaboost算法（HAB优化算法）的半监督图像语义目标对象获取方法。在分析Adaboost算法评估函数不足的基础上，设计并实现HAB优化算法。对比实验结果表明，HAB优化算法在训练误差与抗干扰能力方面具有更好的性能。在此基础上，研究应用HAB优化算法的图像语义目标对象获取方法，从图像对象特征预处理、对象识别器训练、语义对象获取3个方面进行论述。通过实验分析，该方法具有良好的图像目标对象获取性能。

The paper presents a novel approach to extract a semantic image object based on an optimized Harmonious Adaboost algorithm, shortly HAB, which produces less generalization error and high performance compared to the Gentle Adaboost Algorithm. Some key techniques in the proposed schema, including the pre-processing of image character, the training of object detector and the extracting of semantic image object, are discussed. The experiment shows that the recurrent training process improves the performance of the object detector, and the extracting results demonstrate the availability of the work.