图象主要区域的提取是图象语义抽取及其应用的基础.为了更好地进行图象语义的抽取，提出了一种面向图象语义的图象主要区域自动提取方法.该方法首先将图象划分成固定大小的子块，并通过对子块特征进行聚类来获得图象的初始区域分割 ;而后，经过一系列的后处理来优化分割结果，并实现前景和背景区分 ;最后通过分析每个背景区域的重要程度，去除掉不相关的背景区域.通过对包含有显著对象的户外图象进行的实验表明 :该方法不仅可以去除图象中，大量与图象语义不相关的内容，而且能保留图象的主要信息，这就为进一步的图象语义应用打好了基础.

Semantic image retrieval is one of the key technologies to find useful multimedia information more efficiently on Internet or in multimedia database. Extraction of main regions in an image is a precondition for semantic image retrieval. In this article, an automatic approach to extract those main regions is proposed. It first partitions an image into fixed sized blocks, and an elementary segmentation is achieved by clustering the visual features of all the blocks of the image. Then the result of the original segmentation is improved by some extra processing. After that, a special method is employed to distinguish the foreground regions and background regions. Finally, the regions, which are considered not important to the image content, are eliminated, and it is done by analyzing the importance of every region. Our experiments for outdoor images containing relatively salient objects show that, the approach proposed in this paper can get rid of lots of information, which are not related to the image content, and at the same time can also reserve the main useful information for image semantics. It gives a better foundation for the further applications such as image retrieval and image understanding.