Beyond diffusion process: Neighbor set similarity for fast re-ranking

NSS is proposed to replace the role of Diffusion Process to capture the geometry of the underlying manifold in shape and image retrieval.NSS is more precise than Diffusion Process, and more robust to noise.NSS is computed more efficiently than Diffusion Process, and it no longer needs an iterative process to guarantee the retrieval precision.We obtain state-of-the-art retrieval performance on several benchmark datasets. Measuring the similarity between two instances reliably, shape or image, is a challenging problem in shape and image retrieval. In this paper, a simple yet effective method called Neighbor Set Similarity (NSS) is proposed, which is superior to both traditional pairwise similarity and diffusion process. NSS makes full use of contextual information to capture the geometry of the underlying manifold, and obtains a more precise measure than the original pairwise similarity. Moreover, based on NSS, we propose a powerful fusion process to utilize the complementarity of different descriptors to further enhance the retrieval performance. The experimental results on MPEG-7 shape dataset, N-S image dataset and ORL face dataset demonstrate the effectiveness of the proposed method. In addition, the time complexity of NSS is much lower than diffusion process, which suggests that NSS is more suitable for large scale image retrieval than diffusion process.