In order to effectively resolve the "semantic gap" in image retrieval, this paper studied a new method for semantic annotation. Based on image segmentation, the method constructed image dictionary during the training phase, through analysis and description of color, texture and wavelet contour, established the two-stage annotation model combining comparison of wavelet contour and probability. It adopted corresponding method for different images by phases. The experimental results indicate the method can significantly improve recall and precision, and the maximum of precision is 23. 6 percent. Besides, the model can understand image better and it also has good annotation effect and retrieval performance.%为有效解决图像检索中存在的“语义鸿沟”问题,提出了一种新的语义标注方法.该方法以图像分割为基础,在训练阶段构建图像字典,通过对图像单元颜色、纹理、小波轮廓的分析和描述形成一种结合小波轮廓比对和概率统计的二阶段标注模型,模型针对不同类别的图像分阶段采用相应的标注方法.经实验,应用该模型进行图像检索查全率和查准率都有明显提高,其中查准率最高可提升23.6％,证明该方法更接近人对图像内容的理解,具有良好的标注效果和检索性能.
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