A Method of Main-Region Extraction for Semantic Image Retrieve
WANG Hui-feng, SUN Zheng-xing (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093) (Department of Computer Science and Technology, Nanjing University, Nanjing 210093)
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.