This paper proposes a similarity ranking technique that exploits the entire network structure of similarity relationships for multimedia, particularly image, databases. The main problem in the similarity ranking on multimedia is the meaning gap between the characteristics automatically computed from the multimedia dataset and the interpretation by human from the multimedia itself. In fact, the similarity semantics usually lies on high level human interpretation and automatically computed low level multimedia properties may not reflect it. This paper assumes that the meaning of the multimedia is affected by the context or similarity relationships in a dataset and therefore, we propose the ranking technique to catch the semantics from a large multimedia dataset. This similarity ranking technique based on the context or similarity relationships yields better experimental results than the conventional similarity ranking techniques.

本文提出了一种相似性排序技术，该技术利用了多媒体，尤其是图像数据库的相似性关系的整个网络结构。多媒体相似度排名中的主要问题是，从多媒体数据集自动计算出的特征与人对多媒体本身的解释之间的含义差距。实际上，相似性语义通常取决于高级人员解释，而自动计算的低级多媒体属性可能无法反映出来。本文假设多媒体的含义受数据集中上下文或相似关系的影响，因此，我们提出一种排序技术，以从大型多媒体数据集中捕获语义。