In this research two automatic video annotation techniques are considered. The first technique uses ontology to reduce the semantic gap during video retrieval and other performs a group based image retrieval using video files. The proposed algorithm

基于底层特征的图像内容和人为理解的图像语义之间存在“语义鸿沟"的现象，而基于图像底层视觉特征的图像自动标注技术，能够实现从图像的底层特征中提取出高级语义信息的关键字来标注图像，能很好的解决这一难题-Based on the underlying features of image content and image semantic understanding of human existence between the " semantic gap" phenomenon,

图像 自动 标注 通过分析 训练集 ，学习 图像 和标注词 之间的关系，得出 之间的关系，得出 一些规则， 然后使用这些规则 自动 推导出 图像 最适合的标注词 ，这些标注词代表了图像的 ，这些标注词代表了图像的 高层 语义信息 。-Automatic image annotation by analyzing the training set, learning image and mark the relationship between words, the relationship be

the demand for automatically recognizing and retrieving medical image for screening,reference and management is growing faster than ever.In this paper present an intelligent content based image retrieval system called I-Browsewhich integrate both ico