Unsupervised Deep Hashing for Large-scale Visual Search
This addresses the need for efficient visual search in large datasets, but it is incremental as it builds on existing hashing methods with deep learning enhancements.
The paper tackles the problem of learning representative binary codes for large-scale visual search by proposing an unsupervised deep hashing method, achieving competitive results compared to state-of-the-art approaches in experiments.
Learning based hashing plays a pivotal role in large-scale visual search. However, most existing hashing algorithms tend to learn shallow models that do not seek representative binary codes. In this paper, we propose a novel hashing approach based on unsupervised deep learning to hierarchically transform features into hash codes. Within the heterogeneous deep hashing framework, the autoencoder layers with specific constraints are considered to model the nonlinear mapping between features and binary codes. Then, a Restricted Boltzmann Machine (RBM) layer with constraints is utilized to reduce the dimension in the hamming space. Extensive experiments on the problem of visual search demonstrate the competitiveness of our proposed approach compared to state-of-the-art.