Authors: Lucas Pascotti Valem and Daniel Carlos Guimarães Pedronette
Dept. of Statistic, Applied Math. and Computing, Universidade Estadual Paulista (UNESP), Rio Claro, Brazil
A framework for ranked lists selection and fusion, completely unsupervised. If you use this code, please cite our paper:
@article{VALEM2020182,
title = "Unsupervised selective rank fusion for image retrieval tasks",
journal = "Neurocomputing",
volume = "377",
pages = "182 - 199",
year = "2020",
issn = "0925-2312",
doi = "https://doi.org/10.1016/j.neucom.2019.09.065",
url = "http://www.sciencedirect.com/science/article/pii/S0925231219313335",
author = "Lucas Pascotti Valem and Daniel Carlos Guimarães Pedronette",
keywords = "Content-based image retrieval, Unsupervised late fusion, Rank-aggregation, Correlation measure, Effectiveness estimation",
}
You should install the dependencies specified in the "requirements.txt" file and call it like this:
python usrf.py dataset_name
The dataset_name can be flowers, for example.
This repository contains 6 different feature extractions for the OxfordFlowers-17 dataset, which can be used for testing.
In the future, we intend to provide a documentation of how to incorporate custom datasets.
This repository includes a binary file of the UDLF framework, which is licensed under the GPLv2.
There is also Octave, which is an optional dependency in case the user wants to generate some plots for the results.
[usrf.py]
The main file in the project and should be used to call an execution.
[config.py]
Contains all the parameters and dataset configurations (file paths and others).
[stages.py]
Contains the stages of the framework execution
- loading stage
- pre-selection stage
- selection stage
- fusion stage
- evaluation stage
[show_messages.py]
Print messages shown during the USRAF execution.
[rank_dictionaries.py]
From dictionaries, get lists with the values ranked according to the scores.
[load_data.py]
Implements functions to read the data (the ranked lists) from the files into memory.
[parameters_estimation.py]
Estimation of the parameters if enabled in the config are done by the
functions declared in this file.
[correlation_functions.py]
Routines to compute the correlation measures (Jaccard, RBO, and others).
[effectiveness_estimation_functions.py]
Routines to compute the effec. estim. measures (Authority and Reciprocal).
[selection_functions.py]
Routines that implement the selection score.
[tuples_processing.py]
Compute available pairs, tuples, intersection of pairs, and others.
[evaluation_functions.py]
Some functions used in the evaluation stage.
[execute_udlf.py]
Set the parameters and prepare the executions for the UDL framework.
The executions are processed in parallel (using pools).
[udlf/udlf_calls.py]
Functions to call the UDL framework.
[octave_calls.py]
Functions that generate octave scripts and build them.
This is used by the evaluation stage.
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 2017/02091-4 and 2013/08645-0).
This project is licensed under GPLv2. See details.