Guide to Match: Multi-Layer Feature Matching With a Hybrid Gaussian Mixture Model

Abstract

As a fundamental yet challenging task in computer vision, finding correspondences between two sets of feature points has received extensive attention. Among all the proposed methods, the Gaussian Mixture Model (GMM) based algorithms show their great power in formulating such problems. However, they are vulnerable to large portion of outliers in the extracted feature points. In this paper, a new Hybrid Gaussian Mixture Model (HGMM) combined with a multi-layer matching framework is proposed. Different from existing GMM based methods, HGMM uses a set of seed correspondences to guide the matching procedure. To automatically find seed correspondences, the feature points are divided into multiple layers according to their matching potential. With the help of Locality Sensitive Hashing, this can be done economically and efficiently. Correspondences found in lower layers which contain few outliers will be used as hard constraint when matching features in higher layers where a large portion of outliers exist. Extensive experiments show that the proposed method is efficient and more robust to outliers when images have large viewpoint difference or small scene overlap.