Reliable object recognition using SIFT features

SIFT (scale invariant feature transform) features have been one of the most efficient descriptors for object recognition. However, the excessive number of key points and high dimensionality has limited its capacity in object recognition. In this paper we present a novel method based on SIFT features for reliable object recognition. At first, a matching tree is constructed to eliminate non-essential key points. In order to achieve viewpoint independence, a 3D model is constructed for each object in the filtered SIFT feature space. Experimental results on both Caltech 101 and COIL 100 datasets indicate the effectiveness of our proposed algorithm.