基于图论的图像分割技术分析-软件工程专业论文.docx

万方数据 万方数据 Abstract Image segmentation is an important and critical image analysis technique. In image engineering, image segmentation is not only a critical step between image processing and image analysis but also the foundation of further understanding of image. Generally, only the interested part of image which is commonly particular and have unique characteristics is paid attention to when we study images or put them into use. In order to analyze the interested target, first of all, the image is divided into regions which have their own characteristics, and the interested target is extracted from the original separation; then, subsequent steps are done, such as measuring, feature extraction. We call this process as image segmentation. Image segmentation has been a hotspot in the image engineering. There have been thousands of algorithms so far. In this paper, the method based on graph theory is chosen to research, research contents are as follows : In the first place, research background, significance and research status of image segmentation is introduced; In the second place, image preprocessing methods before image segmentation are presented, as the result of image pre-processing has directly effect on the later image segmentation quality, improved methods are highlighted in order to improve the quality of pretreatment in this paper; In the last place, the proposed methods are described in details: firstly, a multi-layer pyramid model diagram is constructed based on image preprocessing. Secondly, construct association model based on semi-supervised learning approach and obtain the similarity matrix. Finally, using normalized segmentation criteria complete image segmentation. In this paper, the proposed image segmentation framework is experimented on Berkeley image database and MSRC image database. The results show that segmentation quality of this method has a certain improvement compared with some traditional, classical methods. Key words: Graph theory, segm