A Frequency Domain Auxiliary Network for Image Retrieval

Image retrieval aims to find the most semantically similar images in the database. Existing deep hash-based retrieval algorithms utilize data augmentation strategies thus generating generalized hash codes. However, simple data augmentation only improves the accuracy of hash codes from the perspective of sample diversity, without fully utilizing the inherent characteristics of the images. In this letter, we explore the frequency domain information of images and propose a Frequency Domain Auxiliary Network (FDANet) for deep hash retrieval. To capture frequency domain information that can cope with image transformations, we develop the spectrum enhancement module (SEM) in FDANet. The SEM utilizes Fourier transform techniques to extract the amplitude component that can reflect the low-level statistics of the image. Then, leveraging the extracted amplitude components, the retrieval network enhances its perception of regions undergoing relative changes in the original spatial domain. Experiments on several image retrieval benchmarks demonstrate that our method outperforms other state-of-the-art hash algorithms in terms of performance on the test metrics.