Supervised Hashing with End-to-End Binary Deep Neural Network
This work addresses efficient image retrieval for large-scale applications, representing an incremental improvement over existing supervised hashing methods.
The authors tackled the problem of supervised image hashing for large-scale visual retrieval by proposing an end-to-end deep network that directly learns binary codes while preserving similarity, independence, and balancing properties, and they demonstrated that their method outperforms state-of-the-art approaches on various benchmarks.
Image hashing is a popular technique applied to large scale content-based visual retrieval due to its compact and efficient binary codes. Our work proposes a new end-to-end deep network architecture for supervised hashing which directly learns binary codes from input images and maintains good properties over binary codes such as similarity preservation, independence, and balancing. Furthermore, we also propose a new learning scheme that can cope with the binary constrained loss function. The proposed algorithm not only is scalable for learning over large-scale datasets but also outperforms state-of-the-art supervised hashing methods, which are illustrated throughout extensive experiments from various image retrieval benchmarks.