The invention discloses an image collaborative segmentation method based on minimum fuzzy divergence, and belongs to the technical field of image processing and computer vision. According to the method, the segmentation effect is judged through an Intersection over Union (IOU) value, and the method comprises the following steps: 1, acquiring an image segmentation data set, and performing conversion from an RGB space to an LAB space; 2, constructing a fuzzy divergence formula by using a Gamma-type membership function, constructing a new energy function, and performing curve evolution accordingto a minimum fuzzy divergence criterion to achieve a good segmentation effect. The target edge is better processed by using the fuzzy set theory. The color information of one image is introduced intothe energy function of the other image, so that the robustness of initial curve replacement can be enhanced. An optimal segmentation effect is achieved by solving a local minimum value of an energy function by utilizing a region-based active contour model. The established model can reduce the complexity of calculation time, and can be applied to early-stage work of an integrated imaging three-dimensional display system.