Patent ID: 11928825
Assignee: TSINGHUA UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. An unsupervised image segmentation method, applied in a terminal device comprising a processor, the method comprising:
performing a superpixel segmentation on an image containing a target object to acquire a plurality of superpixel sets, each superpixel set corresponding to a respective superpixel node;
generating an undirected graph according to a plurality of superpixel nodes corresponding to the plurality of superpixel sets, wherein the undirected graph comprises a first edge connected between two adjacent superpixel nodes, a foreground edge connected between a superpixel node and a virtual foreground node, and a background edge connected between a superpixel node and a virtual background node;
determining foreground superpixel nodes and background superpixel nodes in the undirected graph according to a first label set corresponding to the plurality of superpixel nodes, the foreground superpixel node being a superpixel node belonging to a foreground of the image, and the background superpixel node being a superpixel node belonging to a background of the image;
generating a minimization objective function according to the foreground superpixel nodes and the background superpixel nodes;
segmenting the undirected graph according to the minimization objective function to acquire a foreground part and a background part and to generate a second label set; and
performing an image segmentation on the image according to a comparison result of the first label set and the second label set;
wherein generating the minimization objective function comprises:
determining a weight of the first edge, a weight of the foreground edge, a weight of the background edge of each superpixel node; and
constructing the minimization objective function according to weights of the first, foreground and background edges of the plurality of superpixel nodes;

wherein the method further comprises:
determining the weight of the foreground edge according to a grayscale of the virtual background node, a grayscale of the superpixel node, and a background weight difference; and
determining the weight of the background edge according to a grayscale of the virtual foreground node, the grayscale of the superpixel node, and a foreground weight difference.