Deep Residual Hashing
This addresses the need for efficient large-scale image retrieval in medical imaging, though it appears incremental as it builds on existing hashing and deep learning techniques.
The paper tackled the problem of sub-optimal encoding in image retrieval by proposing Deep Residual Hashing (DRH), an end-to-end deep architecture for supervised hashing, and demonstrated substantial improvements over state-of-the-art methods on a large chest x-ray database.
Hashing aims at generating highly compact similarity preserving code words which are well suited for large-scale image retrieval tasks. Most existing hashing methods first encode the images as a vector of hand-crafted features followed by a separate binarization step to generate hash codes. This two-stage process may produce sub-optimal encoding. In this paper, for the first time, we propose a deep architecture for supervised hashing through residual learning, termed Deep Residual Hashing (DRH), for an end-to-end simultaneous representation learning and hash coding. The DRH model constitutes four key elements: (1) a sub-network with multiple stacked residual blocks; (2) hashing layer for binarization; (3) supervised retrieval loss function based on neighbourhood component analysis for similarity preserving embedding; and (4) hashing related losses and regularisation to control the quantization error and improve the quality of hash coding. We present results of extensive experiments on a large public chest x-ray image database with co-morbidities and discuss the outcome showing substantial improvements over the latest state-of-the art methods.