An Improved SURF and Modified Zernike Moments Descriptor for Object Recognition

Because single local or global characteristics can only depict the classification information of an object unilaterally or partially, that may result in low recognition accuracy; in this paper we propose an improved SURF and modified Zernike moments descriptor (ISMZMD) for object recognition. Firstly, we extracted the improved SURF and seven modified Zernike moments descriptors of objects. Secondly, we effectively fused the two features together with different weight factors based on their contribution to object identification. Thirdly, we computed the Euclidean distance to decide the recognition result. Finally, we evaluated the performance of the proposed algorithm and compared it with other algorithms. The results of the experiments show that our algorithm is effective and robust to scaling alteration, translation change, rotation variation, and noise transformation. Compared with other representative methods, our method has a higher recognition rate and less recognition time.