这是关于语义图像检索进展的一片论文，很好的综述性文章，希望研究图像语义的朋友一起学习- Semantic image retrieval has been a crux to bridge“semantic gap”between the simple visual features and the abundant semantics delivered by an image. The state of the art of semantic image retrieval is g

如何面对人们日常生活中接触到的，尤其是互联网上数量激增的图像进行有 效的分类，已经成为研究的新热点。虽然现有的图像分类技术已经取得不错的性 能，但是它们还存在着一些问题。一是大部分现有的图像分类算法都是基于图像 的底层特征，’无法解决图像分类中的“语义鸿沟”问题；二是，大多数图像分类算 法总是忽视图像中部分与部分之间的空间关系。-How to face contact with people' s daily lives, especially surge in the n

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,