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The currently known point pattern matching algorithms generally performs poorly when the two point patterns to be matched are not isomorphic. To improve the matching performance of the point pattern matching methods for non-isomorphic point patterns, a novel and robust inexact point pattern matching algorithm that combines with the invariant feature and probabilistic relaxation labelling is proposed. A new point-set based invariant feature, Relative Shape Context (RSC), is proposed firstly. Using the test statistic of relative shape context descriptor's matching scores as the foundation of compatibility coefficients, the new support function are constructed based on the compatibility coefficients. Finally, the correct matching results are achieved by using the probabilistic relaxation labelling and imposing the bijective constraints required by the overall correspondence mapping. Experiments on both synthetic point-sets and real image data show that the proposed algorithm is effective and robust.