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This paper addresses the problem of large scale image retrieval, with the aimof accurately ranking the similarity of a large number of images to a givenquery image. To achieve this, we propose a novel Siamese network. This networkconsists of two computational strands, each comprising of a CNN componentfollowed by a Fisher vector component. The CNN component produces dense, deepconvolutional descriptors that are then aggregated by the Fisher Vector method.Crucially, we propose to simultaneously learn both the CNN filter weights andFisher Vector model parameters. This allows us to account for the evolvingdistribution of deep descriptors over the course of the learning process. Weshow that the proposed approach gives significant improvements over thestate-of-the-art methods on the Oxford and Paris image retrieval datasets.Additionally, we provide a baseline performance measure for both these datasetswith the inclusion of 1 million distractors.

Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval