Exploring geometric information in CNN for image retrieval

Convolutional Neural Network (CNN) has brought significant improvements for various multimedia tasks. In contrast, image retrieval has not yet benefited as much since no training database is available. In this paper, we propose an unsupervised weighting scheme for pre-trained CNN models to adaptively emphasize image center. Different from the general preference for fully connected layers which represent abstract semantics, we aggregate the activations of convolutional layers on image patches to depict local patterns in details. It is an empirical observation that the target of searching is naturally the focus of an image. Thus we pooling the features with respect to their positions, since they innately maintain the geometric layout of an image. Experimental results on two benchmarks prove the effectiveness of our methods.