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Robust feature description and matching using local graph

Feature detection and matching are essential parts in most computer vision applications. Many researchers have developed various algorithms to achieve good performance, such as SIFT (Scale-Invariant Feature Transform) and SURF (Speeded Up Robust Features). However, they usually fail when the scene has considerable out-of-plane rotation because they only focus on in-plane rotation and scale invariance. In this paper, we propose a novel feature description algorithm based on local graph representation and graph matching based, which is more robust to out-of-plane rotation. The proposed local graph encodes the geometric correlation between the neighboring features. In addition, we propose an efficient score function to compute the matching score between the local graphs. Experimental result shows that the proposed algorithm is more robust to out-of-plane rotation than conventional algorithms.