Access the official software webpage >>
Authors: Lucas Pascotti Valem and Daniel Carlos Guimarães Pedronette
Dept. of Statistic, Applied Math. and Computing, Universidade Estadual Paulista (UNESP), Rio Claro, Brazil
- Overview
- Get Started
- Binaries
- Compilation
- Execution
- Documentation
- Contributing
- Cite
- Contact
- Acknowledgments
- License
A framework of unsupervised distance learning methods for image and multimedia retrieval tasks. Currently, eleven different unsupervised learning methods are implemented (RFE, RDPAC, BFSTree, LHRR, ContextRR, Correlation Graph, CPRR, Rk Graph Dist., ReckNNGraph, RL-Recom, and RL-Sim*).
An easy guide for your first use can be found in the software official webpage.
Binaries are available for download in the release page.
This project can be compiled by any C++ compiler that supports the C++2014 standard. There is a Makefile that can be used to compile the code. A executable called udlf
will be generate inside the bin/ directory.
The executable is called in the terminal:
-
Linux and MacOS:
./udlf [config.ini]
-
Windows:
call udlf.exe [config.ini]
NOTE: The binary must be executed inside the bin/ directory.
The configuration file specifies everything about the execution:
the desired task, method being used, dataset information, input files,
output files,
evaluation settings,
and other details.
When the binary is executed, it searchs for a config.ini
file in its current directory. The user can also specify a different
configuration file that can be passed as a parameter: ./udlf my_conf.ini.
The software considers only a single configuration file per execution.
NOTE: Complete examples of input files for distinct datasets are available here.
After the execution, a log.txt
is generated:
- GENERAL INFORMATION -
--------------------------------------
Task: UDL
Method: CPRR
Dataset Size: 1400
Image List File: desc/lists/mpeg7.txt
Image Class File: desc/classes/mpeg7.txt
Input File: desc/matrices/mpeg7/cfd.txt
Input Format: MATRIX DIST
Output File: output/output
Output Format: RK ALL
--------------------------------------
- METHOD PARAMETERS -
--------------------------------------
PARAM_CPRR_K = 20
PARAM_CPRR_L = 400
PARAM_CPRR_T = 2
--------------------------------------
- EVALUATION RESULTS -
--------------------------------------
* Efficiency: Total Time of the Algorithm Execution: 0.0438 s
* Effectiveness:
Before:
P@20 0.7559
Recall@40 0.8444
MAP 0.8064
After:
P@20 0.8979
Recall@40 0.9477
MAP 0.9215
Relative Gains:
P@20 18.7866%
Recall@40 12.2404%
MAP 14.2707%
--------------------------------------
Log generated at 2017/1/26 16:37:24
The results can be exported in different formats. Below you can see some examples of ranked lists that were exported as a html page. The query images are presented in green borders and wrong results in red borders. The first line represents the original retrieval results and the second line, the results after the algorithm execution.
NOTE: The above examples consider the datasets Corel5k, MPEG-7, Oxford17Flowers, and Soccer; respectively.
The documentation is available in the software wiki.
We appreciate suggestions, ideas and contributions. If you want to contribute, feel free to contact us. Github pull requests should be avoided because they are not part of our review process. To report small bugs, you can use the issue tracker provided by GitHub.
If you use this software, please cite
@inproceedings{Valem:2017:UDL:3078971.3079017,
author = {Valem, Lucas Pascotti and Pedronette, Daniel Carlos Guimar\~{a}es},
title = {An Unsupervised Distance Learning Framework for Multimedia Retrieval},
booktitle = {Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval},
series = {ICMR '17},
year = {2017},
isbn = {978-1-4503-4701-3},
location = {Bucharest, Romania},
pages = {107--111},
numpages = {5},
url = {http://doi.acm.org/10.1145/3078971.3079017},
doi = {10.1145/3078971.3079017},
acmid = {3079017},
publisher = {ACM},
address = {New York, NY, USA},
}
Lucas Pascotti Valem: lucaspascottivalem@gmail.com
or lucas.valem@unesp.br
Daniel Carlos Guimarães Pedronette: daniel.pedronette@unesp.br
The authors are grateful to São Paulo Research Foundation - FAPESP (grants 2013/08645-0, and 2014/04220-8).
This project is licensed under GPLv2. See details.