Fast landmark image retrieval based on Gaussian mixture clustering

Due to the increasing scale of the reference landmark image database, the efficiency of image retrieval tasks is significantly reduced. In this work, we propose an image retrieval method based on feature clustering to improve the efficiency of largescale image retrieval. Firstly, The Deep Orthogonal Local and Global(DOLG)information fusion framework is used to extract features from images. Secondly, the optimal number of clusters is determined by Bayesian. Then, the image features are divided into different clusters by the Gaussian mixture clustering algorithm. After that, the feature of the queried image will be compared with all centers of clusters and retrieved in the luster with the nearest cluster center. Finally, the retrieved images are reordered by the contextual similarity aggregated with the self-attention mechanism; the most similar referenced images are regarded as the retrieval result. Experimental results on the Roxford5k and Rparis6k datasets demonstrate that the proposed feature clustering-based image retrieval method can significantly reduce the time cost by more than 26% without loss of accuracy.