Research On Image Segmentation Optimization Algorithm Of Scene Labeling

With the popularization of Internet and the wide application of image processing equipments, image, vedio and other multimedia information play a more important role in people’s life and work. As image information is intuitionistic and intelligible, the effective management and retrieval of image information has gradually become the focus of researh. At present, there is a growing demand for the high-level semantic expression of the image, but the traditional method is to manually label the image, which not only consumes time and labor, but also is prone to ambiguity. Therefore, image annotation based on semantics comes into being and rapidly develops, which promoting the development of image undering and retrieval. Most of the current image annotation algorithms achieve via feature similarity of the whole image and relevance of semantic keywords, but they are not the recognition of objects and scenes of the image. Scene labeling is the process of labeling all pixels in the image. It not only realizes the automatic semantic description of the image, but also achieves the complete analysis of object and scene of the image.In order to promote the accuracy of scene labeling of the image, this paper proposes a method of mapping from image features to advanced semantic expression based on image segmentation via the analysis of correlated algorithms. The content of this research is mainly following two parts: The first part is the optimization of image segmentation algorithm which is the basic of scene labeling via the analysis of various image segmentation algorithms. It achieves the optimization of paremeters in image segmentation process using SVM(Support Vector Machine) and the contour feature of the image. The second part is scene labing based on image segmentation and achieving mapping from objects in the scene to semantics using features of segmentation regions, and confirms the fact that the effect of segmentation impacts on the accuracy of scene labeling. Using the multiscale feature, this paper achieves the process of image segmentation and scene labeling, and utilizes image features under different parameters and resolution ratios to determine the location of boundary and the classification of regions. According to experiments on traditional image datasets, this paper validate s the superiority of the improved image segmentation algorithm and the effectiveness of the improvement of the scene labeling algorithm using image segmentation based on visual evaluation and rating scale.