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中文摘要：
图像自动标注是图像理解与模式识别等领域中具有挑战性的关键研究问题．目前图像自动标注领域存在着一些问题．如未标注数据规模要远大于标注数据规模，只能单独使用某种图像分割策略与某类图像表示方法．针对上述问题，提出了基于Co—training的图像自动标注方法，通过构建4个独立的特征属性进而建立4个子分类器，将不同的图像分割方法与特征表示方法整合到一个统一框架中，利用提出的基于投票与一致性相结合的自适应算法扩展原始训练集．该方法通过使用Co—training算法，利用大量未标注数据来提升图像自动标注的性能．通过在Core[5K数据库上进行实验，验证了提出方法的有效性．
英文摘要：
Automatic image annotation is a critical and challenging problem in pattern recognition and image understanding areas. There are some problems in existing automatic image annotation areas. For example, the size of unlabeled data is much larger than the labeled data. Besides, most image annotation models can only use one kind of image segmentation strategy and certain image descrip- tion method. According to above problems, an automatic image annotation model based on Co-training is proposed. In this model, four independent feature properties are constructed and then four corresponding sub-classifiers are built. In this way, different image seg- mentation strategies and feature representation methods can be integrated into a unified framework. An adaptive algorithm based on vote and consistency is proposed to extend the training dataset. The proposed method use Co-training algorithm and mass unlabeled data to improve the performance of automatic image annotation. Experiments conducted on Corel 5K dataset verify the effectiveness of proposed method.
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