The invention discloses a robust model fitting method based on global greedy search. The robust model fitting method specifically comprises the steps that a data set is arranged, and parameters are initialized; a label is used for obtaining an inner point of an [m]th model instance of which the class label is m; according to a sampling method of the global greedy search, model hypotheses theta aregenerated on I<m> and input data x or the model hypotheses theta are generated on data of which the class label is 0 in the label according to a sampling method of an HMSS; a new label is obtained according to the model hypotheses theta and the label; m<c> recently generated model hypotheses are merged together to obtain an [m-tilde]<c> model hypothesis, then the [m-tilde]<c> model hypothesis isused for obtaining the new label; and the m<c> generated model hypotheses are output, and according to outputting of the m<c> generated model hypotheses, an image is segmented to complete the model fitting. The robust model fitting method selects data subsets from the inner points to generate more accurate initial model hypotheses, and can be applied to computer visual tasks such as homography matrix estimation, fundamental matrix estimation, two-view plane segmentation and motion segmentation.