A Cooperative Image Segmentation Method Based on Minimum Fuzzy Divergence

Abstract

An image collaborative segmentation method based on the minimum fuzzy divergence belongs to the technical field of image processing and computer vision. The present invention judges the segmentation effect by the Intersection over Union (IOU) value. 1. Obtain the image segmentation data set and perform RGB space To LAB space conversion; 2. Utilize Gamma type membership function to construct fuzzy divergence formula, construct new energy function, carry out curve evolution according to minimum fuzzy divergence criterion, reach good segmentation effect; The present invention utilizes fuzzy set theory to better Processing the edge of the target; introducing the color information of one image into the energy function of another image can enhance the robustness of the initial curve replacement; using the region-based active contour model, by solving the local minimum of the energy function to achieve Optimal segmentation effect; the established model can reduce the complexity of calculation time, and can be applied to the preliminary work of integrated imaging stereoscopic display system.

Description

technical field [0001] The invention belongs to the technical field of image processing and computer vision, and in particular relates to an image cooperative segmentation method based on minimum fuzzy divergence. Background technique [0002] With the increasing scale of image data in recent years, the demand for image segmentation in many practical applications is no longer satisfied with small-scale single image segmentation. Extracting common objects from a group of images has become an active research topic, namely, image segmentation. Co-segmentation problem. [0003] The idea of ​​collaborative segmentation is to consider an additional foreground similarity constraint on the segmentation method based on a single image, so as to realize the segmentation of common objects. In response to this problem, a variety of collaborative segmentation algorithms have been proposed in the past few years, and have been successfully applied to practical problems, such as the collabo...

AI Technical Summary

Problems solved by technology

In response to this problem, a variety of collaborative segmentation algorithms have been proposed in the past few years, and have been successfully applied to practical problems, such as the collaborative method based on Markov Random Field (MRF), according to the optimal criterion in estimation theory To determine the objective function of the segmentation problem and transform the segmentation problem into an optimization problem, the difficulty lies in the selection of image feature selection and the selection of the foreground similarity measurement method; the collaborative method based on thermal diffusion is to model the segmentation problem as the maximum temperature of thermal diffusion optimization problem, but the effect is not ideal for the foreground (such as the human body) composed of multiple unique regions; the collaborative method based on random walk, the optimization calculation is simpler, but the user needs to mark the foreground region and Background area; in the collaborative method based on active contours, different feature selection methods and consistency measurement methods will have different effects on the model

[0004] Due to the defects of the above algorithm, it is difficult to achieve ideal results in practical applications, so it is necessary to improve

Images