Research On Interactive Image Segmentation Algorithms Based On Graph Theory And Label Transfer

Interactive image segmentation refers to dividing the image into a number of homogeneous connected areas with strong internal consistency and different characteristics according to certain similarity criteria based on a priori knowledge provided by the user,and one or more of interest to the user Each target semantic area and contour are described,and finally they are separated from the complex background environment.Most are generally based on the local pixel relationship to build a segmentation model,which cannot capture long-distance information of the image,it is difficult to obtain a complete target contour,and is prone to under-segmentation;label prior(seed point)information based on the local similarity relationship diagram Although the transfer strategy can effectively transfer the similarity relationship within the class,it is also difficult to effectively preserve the difference between the classes,and it is difficult to ensure the accurate transfer of the label information;in addition,the existing segmentation methods are also very sensitive to the user’s interactive information,usually manifested as The more interactions the user provides,the better the segmentation effect.The position of the seed point provided by the user needs to be completely correct.Once the wrong marking situation occurs(such as marking the background pixel as the foreground seed point),the algorithm cannot obtain the correct segmentation result.In order to obtain satisfactory results,users need to make additional interaction efforts.In view of the above problems,this paper has conducted an in-depth exploration of label transfer and fault-tolerant model construction by deeply studying graph theory interactive image segmentation methods,and proposed an interactive image segmentation algorithm based on label pair transfer and an error Mark-tolerant interactive image segmentation algorithm,and a large number of comparative experiments prove the effectiveness of the proposed model.The main work done in this article is:(1)A label pair transfer algorithm for interactive image segmentation tasks is proposed.Compared with label transfer,this method can use higher-order information to more accurately capture the relationship between unlabeled data and labeled data on tensor product graphs,and explore more complex interactions between image elements and image element pairs.More elaborate relationship with tag pairs.In the image segmentation algorithm based on label pair transfer,first establish an a priori label estimation framework to calculate the a priori probability of the label pair;then use the probability learning process based on tensor product graph to smooth the a priori label;in order to maintain the The computational efficiency of the algorithm,the probability learning process based on tensor product graph is equivalent to the iterative label pair transfer process on the original graph;finally,based on the total probability theorem,the probability of the binary superpixel pair is converted into the probability of the unipixel superpixel.Finally,the qualitative and quantitative analysis of the experiment proves that the proposed method is superior to the latest interactive image segmentation method in performance.(2)A label fault-tolerant interactive image segmentation model is constructed to eliminate the negative impact of segmentation results caused by wrong user marks.In order to accurately estimate the label prior probability under the condition of wrong user labeling,reliability learning is constructed by assigning smaller weights to wrong clusters and larger weights to correct clusters with higher matching degrees model.Finally,the weighted average of all clusters is used to obtain the accurate label prior probability.Whether the user interaction is used as a hard constraint or a priori label estimation,the fault-tolerance model proposed in this paper can be fault-tolerant.Finally,through a large number of comparative experiments,it is confirmed that this method has higher accuracy and efficiency when the wrong mark appears.