中文摘要：

为了充分利用标注词间的相关性,提高图像标注精度,解决图像检索中的语义鸿沟问题,提出了一种基于联合媒体相关模型的图像自动标注改进算法.该算法将标注词和图像的联合概率求解过程转换成在标注词条件下图像出现的概率和标注词的先验概率的求解过程,减少了高频候选标注词对概率统计模型的影响,同时引入语义相似语言模型,利用上下文关联词矢量表示每个标注词,通过估计1幅图像的1组相关性最大的标注词来实现对图像的标注.与基于联合媒体相关图像自动标注算法相比,在标注过程中,本算法不再假设模型中各标注词之间是相互独立的,充分考虑标注词上下文的相关性信息,提高了图像标注精度;对标准的Corel图像集实验结果表明,基于联合媒体相关模型的图像自动标注改进算法是有效的.

英文摘要：

A image annotation algorithm based on Cross Media Relevance Model was proposed to bridge the semantic gap of content-based image retrieval. The algorithm reduced the word bias observed in probabilistic models by converting the word-image joint probability to image probability conditioned on annotation words and estimated the probability of a set of annotation words by measuring the semantic similarities of each annotation word to all other word. It used a contextual term vector to represent a annotation word, and implemented image annotation by estimating maximum correlation between an image and a set of annotation words. Compared with image annotation algorithm based on Cross Media Relevance Model, the proposed algorithm stopped making the assumption that each keyword was independent to each other, instead, took contextual relevance information of annotation words into account. Experimental results on the typical corel dataset demonstrated the effectiveness and the increasing annotation precision of the proposed algorithm.