A software library for locality sensitive hashing vectors using the mathematics of an A* lattice.
See the file LICENCE.
Windows
- Install Visual Studio.
- Open
lib_source\AStarNN.sln. - Build for appropriate configurations and platforms.
- DLL files should be automatically copied to directory
astarnn.
Linux
- Run:
cd lib_source - Run:
make install - Shared library files should be automatically copied to directory
astarnn.
Macintosh
- Ensure developer tools (gcc and make) are installed.
- Open a terminal window.
- Run:
cd lib_source - Run:
make install - Shared library files should be automatically copied to directory
astarnn.
- Ensure an appropriate shared library is installed in directory
astarnn. See Compiling from source above. - Ensure Python is installed.
- Ensure directory
astarnnis in the PYTHONPATH. If using Pycharm, markastarnnas a source directory in the project structure. - Unit tests may be run using the script
astarnn/all_test.py. - Demo scripts in the directory
demo.
Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search. Lv, Q., Josephson, W., Wang, Z., Charikar, M., & Li, K. Proceedings of the 33rd international conference on Very large data bases, 2007; pp. 950-961 https://www.cs.princeton.edu/courses/archive/spring13/cos598C/p950-lv.pdf
A Time-Space Efficient Locality Sensitive Hashing Method for Similarity Search in High Dimensions. Lv, Q., Josephson, W., Wang, Z., Charikar, M., Li, K. Princeton University, Princeton, June 2006. https://www.cs.princeton.edu/techreports/2006/759.pdf
Query adaptative locality sensitive hashing. Jégou, H., Amsaleg, L., Schmid, C., Gros, P. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2008; p 825-828. https://inria.hal.science/inria-00318614/document
Vector Quantising Feature Space with a Regular Lattice. Tuytelaars, T., Schmid, C. Proceedings from 11th IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, October 2007. https://inria.hal.science/inria-00548675/document
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions. Andoni, A., Indyk, P. Proceedings from 47th Annual IEEE Symposium on Foundations of Computer Science, Berkeley, California, USA, 2006; p459-468. Communications of the ACM, Volume 51, Issue 1, pp 117–122. https://doi.org/10.1145/1327452.1327494
Fast approximate nearest neighbors with automatic algorithm configuration. Muja, M., Lowe, D.G. Proceedings from International Conference on Computer Vision Theory and Applications (VISAPP 2009), Lisboa, Portugal, February 2009. https://lear.inrialpes.fr/~douze/enseignement/2014-2015/presentation_papers/muja_flann.pdf
Entropy based nearest neighbor search in high dimensions. Panigrahy, R. In Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithms, (SODA 2006), Miami, Florida, USA, 2006; ACM: 2006; p 1195. https://arxiv.org/pdf/cs/0510019.pdf
Locality-sensitive hashing scheme based on p-stable distributions. Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S. In Proceedings of the twentieth annual symposium on Computational geometry, 2004; p253-262. https://www.cs.princeton.edu/courses/archive/spring05/cos598E/bib/p253-datar.pdf
Memory Efficient Recognition of Specific Objects with Local Features. Kise, K., Noguchi, K., Iwamura, M. In Proceedings from 19th International Conference on Pattern Recognition, Tampa, Florida, USA, 2008; IEEE. https://ieeexplore.ieee.org/document/4761711
Simple Representation and Approximate Search of Feature Vectors for Large-Scale Object Recognition. Kise, K., Noguchi, K., Iwamura, M. In Proceedings 18th British Machine Vision Conference, (BMVC 2007), UK, September 2007; p 182-191. https://www.dcs.warwick.ac.uk/bmvc2007/proceedings/CD-ROM/papers/paper-231.pdf
Image retrieval method. Barry Drake, Scott Rudkin, Alan Tonisson. 2011. https://patents.google.com/patent/US10289702B2
Method, system and apparatus for generating hash codes. Barry Drake, Andrew Downing. 2015. https://patents.google.com/patent/US20170075887A1/