Deep Feature Pyramid Hashing for Efficient Image Retrieval

Abstract

Thanks to the success of deep learning, deep hashing has recently evolved as a leading method for large-scale image retrieval. Most existing hashing methods use the last layer to extract semantic information from the input image. However, these methods have deficiencies because semantic features extracted from the last layer lack local information, which might impact the global system’s performance. To this end, a Deep Feature Pyramid Hashing DFPH is proposed in this study, which can fully utilize images’ multi-level visual and semantic information. Our architecture applies a new feature pyramid network designed for deep hashing to the VGG-19 model, so the model becomes able to learn the hash codes from various feature scales and then fuse them to create final binary hash codes. The experimental results performed on two widely used image retrieval datasets demonstrate the superiority of our method.