There is the image hashing toolkit, which contains the following methods. You will need to download the images (mat format) at here, and run main.m.
- JPSH: Binary Representation via Jointly Personalized Sparse Hashing (TOMM, 2022).
- CH: Concatenation hashing: A relative position preserving method for learning binary codes (PR, 2020).
- RSSH: Unsupervised hashing based on the recovery of subspace structures (PR, 2020).
- JSH: Jointly sparse hashing for image retrieval (TIP, 2018).
- OCH: Ordinal constrained binary code learning for nearest neighbor search (AAAI, 2017).
- LGHSR: Large graph hashing with spectral rotation (AAAI, 2017).
- ADLLH: Toward optimal manifold hashing via discrete locally linear embedding (TIP, 2017).
- OEH: Towards optimal binary code learning via ordinal embedding (AAAI, 2016).
- IMH: Hashing on nonlinear manifolds (TIP, 2015).
- SP: Sparse projections for highdimensional binary codes (CVPR, 2015).
- SGH: Scalable graph hashing with feature transformation (IJCAI, 2015).
- ITQ: Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval (TPAMI, 2012).
- AGH: Hashing with graphs (ICML, 2011).
- SH: Spectral hashing (NeurIPS, 2008).
- LSH: Locality-sensitive hashing scheme based on p-stable distributions (ASCG, 2004).