Local image feature matching for object recognition

We present a method for matching image local features, specifically SIFT features, to a database of learned object features for the purpose of object recognition and localisation. Our approach differs from existing methods by taking into account the geometric consistency of matched features concurrently with their description vector similarity. As a result we do not need to over-constrain the description vector matching criteria (description vectors of matching features do not need to be nearest neighbours). The outcome of our approach is a greater number of feature matches between a scene image and a database image, as well an improvement in matching speed under certain circumstances.