Large-scale vocabularies with local graph diffusion and mode seeking

In this work, we propose a large-scale clustering method that captures the intrinsic manifold structure of local features by graph diffusion for image retrieval. The proposed method is a mode seeking like algorithm, and it finds the mode of each data point with the defined stochastic matrix resulted by a same local graph diffusion process. While mode seeking algorithms are normally costly, our method is efficient to generate large-scale vocabularies as it is not iterative, and the major computational steps are done in parallel. Furthermore, unlike other clustering methods, such as k-means and spectral clustering, the proposed clustering algorithm does not need to empirically appoint the number of clusters beforehand, and its time complexity is independent on the number of clusters. Experimental results on standard image retrieval datasets demonstrate that the proposed method compares favorably to previous large-scale clustering methods.