基于内容的图象检索系统，其目标是最大限度地减小图象简单视觉特征与用户检索丰富语义之间的“语义鸿沟”，因此图象语义处理则成为基于内容的图象检索进一步发展的关键。为了使人们对基于内容的图象检索中的语义处理方法有个概略了解，首先从图象语义模型和图象语义提取方法这两个方面对利用语义进行图象检索的研究状况进行了总结，并将图象语义模型概括为图象语义知识、图象语义层次模型和语义抽取模型等3个主要组成部分；然后将图象语义提取方法分为用户交互、将查询请求作为语义模板、对象及其空间关系、场景和行为语义及情感语义等类别，同时对其中有代表性的方法进行了详细的分析，还指出了其局限性；最后从对象建模和识别、语义抽取规则和用户检索模型3个方面，阐明了实现图象语义处理所面临的问题，并提出了一些初步的解决思路。

The goal of content-based image retrieval(CBIR) is uppermost to reduce the semantic gap between the simple visual features and the abundant semantics delivered by an image, and a critical point in the advancement of content-based retrieval is the image semantic modeling and extraction. This paper reviews the state of the art of image retrieval using semantics mainly focusing upon two aspects: image semantic modeling and image semantic extraction. In the paper, image semantic model is generalized firstly as three main components: image semantic knowledge, image semantic hierarchical model and semantic extraction hierarchical model. Then some typical methods of semantics extraction of are analyzed in detail by classifying into five classes: semantic by user interaction, user query as visual semantic template, objects and their layout recognition, scene and event semantics extraction, emotion semantics extraction, and some limitation of them are pointed out. Finally, three critical problems including object modeling and recognition, semantic knowledge base and user retrieval model faced in image semantic processing are explained, and some resolved strategies are presented elementarily.