Intra-class variation, affine transformation and background clutter: towards robust image matching

Image matching is a fundamental computer vision problem that includes many scenarios, such as varying the view scene matching, feature selection and registration, object recognition, and general object class matching. This article presents a unified framework and working algorithm for these different matching scenarios. The proposed feature-based image matching method demonstrates excellent robustness to significant geometrical transformation, intra-class variation and background clutter which are usually presented in different matching scenarios.