Feature Matching With Intra-Group Sparse Model.

Feature matching is a fundamental problem in computer vision area. In many real applications, one can usually obtain some potential (candidate) matches $\mathcal {C}$ by using some discriminative feature descriptors, such as SIFT descriptor. Then, the feature matching problem can be formulated as the problem of trying to select the correct matches $\mathcal {S}$ from the potential match set $\mathcal {C}$. In this paper, we propose to solve matches selection by developing a novel intra-group sparse matching (IGSM) model. Our IGSM is motivated by a simple observation that the potential match set $\mathcal {C}$ can be divided into several non-overlapping groups $\mathcal {C}_i$ , among which the correct matches $\mathcal {S}$ are uniformly distributed. We thus develop an intra-group selection model to conduct matches selection at the intra-group level to incorporate the one-to-one matching constraint more in matches selection process. Our IGSM model has three main advantages: (1) The selection mechanism is parameter-free; (2) it generates an intra-group sparse solution which better maintains the one-to-one matching constraint in nature; (3) a simple yet effective update algorithm has been derived to solve IGSM model. The optimality and convergence of the algorithm are theoretically guaranteed. Experimental results on several image feature matching datasets show the effectiveness and efficiency of the proposed IGSM matching method. [ABSTRACT FROM AUTHOR]