Robust image matching via feature guided Gaussian mixture model | IEEE Conference Publication | IEEE Xplore

Robust image matching via feature guided Gaussian mixture model


Abstract:

In this paper, we propose a novel feature guided Gaussian mixture model (FG-GMM) for image matching, which typically requires matching two sets of feature points extracte...Show More

Abstract:

In this paper, we propose a novel feature guided Gaussian mixture model (FG-GMM) for image matching, which typically requires matching two sets of feature points extracted from the given images. We formulate the problem as estimation of a feature guided mixture of densities: a GMM is fitted to one point set, such that both the centers and local features of the Gaussian densities are constrained to coincide with the other point set. The problem is solved under a unified maximum-likelihood framework together with an iterative semi-supervised Expectation-Maximization (EM) algorithm initialized by the confident feature correspondences. The image transformation is specified in a reproducing kernel Hilbert space and a sparse approximation is adopted to achieve a fast implementation. Extensive experiments on various real images show the robustness of our approach, which consistently outperforms other state-of-the-art methods.
Date of Conference: 11-15 July 2016
Date Added to IEEE Xplore: 29 August 2016
ISBN Information:
Electronic ISSN: 1945-788X
Conference Location: Seattle, WA, USA

1. Introduction

Establishing reliable correspondence between two images is a fundamental problem in computer vision and multimedia, and it is a critical prerequisite in a wide range of applications including 3D reconstruction, tracking, super-resolution, content based image retrieval [1]–[8]. In this paper, we formulate it as a matching problem between two sets of discrete points where each point is an image feature, extracted by a feature detector, and has a local image descriptor, such as Scale Invariant Feature Transform (SIFT) [9].

References

References is not available for this document.