2024
J. P. Martinez-Esteso F. J. Castellanos, A. Galán-Cuenca
A Region-Based Approach for Layout Analysis of Music Score Images in Scarce Data Scenarios Proceedings Article
In: Lecture Notes in Computer Science - International Conference on Document Analysis and Recognition, pp. 58-75, Springer, Athenes, Greece, 2024, ISBN: 978-3-031-70545-8.
@inproceedings{Castellanos:2024:scarceLA,
title = {A Region-Based Approach for Layout Analysis of Music Score Images in Scarce Data Scenarios},
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2023
A. J. Gallego F. J. Castellanos, I. Fujinaga
A Few-Shot Neural Approach for Layout Analysis of Music Score Images Proceedings Article
In: Proceedings of the 24th International Society for Music Information Retrieval Conference, pp. 106-113, Milan, Italy, 2023, ISBN: 978-1-7327299-3-3.
@inproceedings{Castellanos:2023:few,
title = {A Few-Shot Neural Approach for Layout Analysis of Music Score Images},
author = {F. J. Castellanos, A. J. Gallego, I. Fujinaga},
url = {https://archives.ismir.net/ismir2023/paper/000011.pdf},
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}
A. J. Gallego F. J. Castellanos, I. Fujinaga
A Preliminary Study of Few-shot Learning for Layout Analysis of Music Scores Working paper
2023.
Abstract | Links | BibTeX | Tags: DOREMI
@workingpaper{Castellanos:2023:preFew,
title = {A Preliminary Study of Few-shot Learning for Layout Analysis of Music Scores},
author = {F. J. Castellanos, A. J. Gallego, I. Fujinaga},
url = {https://arxiv.org/pdf/2311.04091#page=45},
year = {2023},
date = {2023-11-04},
urldate = {2023-11-04},
issue = {WORMS},
abstract = {Proceedings of the 5th International Workshop on Reading Music Systems, Milan, Italy},
keywords = {DOREMI},
pubstate = {published},
tppubtype = {workingpaper}
}
Proceedings of the 5th International Workshop on Reading Music Systems, Milan, Italy Penarrubia, C.; Valero-Mas, J. J.; Gallego, A. J.; Calvo-Zaragoza, J.
Addressing Class Imbalance in Multilabel Prototype Generation for k-Nearest Neighbor Classification Conference
Iberian Conference on Pattern Recognition and Image Analysis, Alicante, Spain, 2023, ISBN: 978-3-031-36616-1.
Abstract | Links | BibTeX | Tags: DOREMI
@conference{nokey,
title = {Addressing Class Imbalance in Multilabel Prototype Generation for k-Nearest Neighbor Classification},
author = {C. Penarrubia and J. J. Valero-Mas and A. J. Gallego and J. Calvo-Zaragoza},
doi = {https://doi.org/10.1007/978-3-031-36616-1_2},
isbn = {978-3-031-36616-1},
year = {2023},
date = {2023-06-25},
booktitle = {Iberian Conference on Pattern Recognition and Image Analysis},
pages = {15.27},
address = {Alicante, Spain},
abstract = {Prototype Generation (PG) methods seek to improve the efficiency of the k-Nearest Neighbor (kNN) classifier by obtaining a reduced version of a given reference dataset following certain heuristics. Despite being largely addressed topic in multiclass scenarios, few works deal with PG in multilabel environments. Hence, the existing proposals exhibit a number of limitations, being label imbalance one of paramount relevance as it constitutes a typical challenge of multilabel datasets. This work proposes two novel merging policies for multilabel PG schemes specifically devised for label imbalance, as well as a mechanism to prevent inappropriate samples from undergoing a reduction process. These proposals are applied to three existing multilabel PG methods—Multilabel Reduction through Homogeneous Clustering, Multilabel Chen, and Multilabel Reduction through Space Partitioning—and evaluated on 12 different data assortments with different degrees of label imbalance. The results prove that the proposals overcome—in some cases in a significant manner—those obtained with the original methods, hence validating the presented approaches and enabling further research lines on this topic.},
keywords = {DOREMI},
pubstate = {published},
tppubtype = {conference}
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Prototype Generation (PG) methods seek to improve the efficiency of the k-Nearest Neighbor (kNN) classifier by obtaining a reduced version of a given reference dataset following certain heuristics. Despite being largely addressed topic in multiclass scenarios, few works deal with PG in multilabel environments. Hence, the existing proposals exhibit a number of limitations, being label imbalance one of paramount relevance as it constitutes a typical challenge of multilabel datasets. This work proposes two novel merging policies for multilabel PG schemes specifically devised for label imbalance, as well as a mechanism to prevent inappropriate samples from undergoing a reduction process. These proposals are applied to three existing multilabel PG methods—Multilabel Reduction through Homogeneous Clustering, Multilabel Chen, and Multilabel Reduction through Space Partitioning—and evaluated on 12 different data assortments with different degrees of label imbalance. The results prove that the proposals overcome—in some cases in a significant manner—those obtained with the original methods, hence validating the presented approaches and enabling further research lines on this topic.F. J. Castellanos A. Rosello, J. P. Martinez-Esteso
Test-Time Augmentation for Document Image Binarization Proceedings Article
In: Iberian Conference on Pattern Recognition and Image Analysis, pp. 158-169, Springer Nature Switzerland, 2023, ISBN: 978-3-031-36615-4.
@inproceedings{Rosello2023:test-time,
title = {Test-Time Augmentation for Document Image Binarization},
author = {A. Rosello, F. J. Castellanos, J. P. Martinez-Esteso, A. J. Gallego, J. Calvo-Zaragoza},
doi = {https://doi.org/10.1007/978-3-031-36616-1_13},
isbn = {978-3-031-36615-4},
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publisher = {Springer Nature Switzerland},
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Valero-Mas, J. J.; Gallego, A. J.; Alonso-Jiménez, P.; Serra, X.
Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification Journal Article
In: Pattern Recognition, vol. 135, pp. 109190, 2023, ISSN: 0031-3203.
Abstract | Links | BibTeX | Tags: DOREMI, MultiScore
@article{k519,
title = {Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification},
author = {J. J. Valero-Mas and A. J. Gallego and P. Alonso-Jiménez and X. Serra},
doi = {https://doi.org/10.1016/j.patcog.2022.109190},
issn = {0031-3203},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Pattern Recognition},
volume = {135},
pages = {109190},
abstract = {Prototype Generation (PG) methods are typically considered for improving the efficiency of the k-Nearest Neighbour (kNN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel kNN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving—both in terms of efficiency and classification performance—the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.},
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Prototype Generation (PG) methods are typically considered for improving the efficiency of the k-Nearest Neighbour (kNN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel kNN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving—both in terms of efficiency and classification performance—the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.
2024
J. P. Martinez-Esteso F. J. Castellanos, A. Galán-Cuenca
A Region-Based Approach for Layout Analysis of Music Score Images in Scarce Data Scenarios Proceedings Article
In: Lecture Notes in Computer Science - International Conference on Document Analysis and Recognition, pp. 58-75, Springer, Athenes, Greece, 2024, ISBN: 978-3-031-70545-8.
@inproceedings{Castellanos:2024:scarceLA,
title = {A Region-Based Approach for Layout Analysis of Music Score Images in Scarce Data Scenarios},
author = {F. J. Castellanos, J. P. Martinez-Esteso, A. Galán-Cuenca, A. J. Gallego},
url = {https://link.springer.com/chapter/10.1007/978-3-031-70546-5_4},
doi = {https://doi.org/10.1007/978-3-031-70546-5_4},
isbn = {978-3-031-70545-8},
year = {2024},
date = {2024-09-11},
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booktitle = {Lecture Notes in Computer Science - International Conference on Document Analysis and Recognition},
volume = {14807},
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publisher = {Springer},
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keywords = {DOREMI},
pubstate = {published},
tppubtype = {inproceedings}
}
2023
A. J. Gallego F. J. Castellanos, I. Fujinaga
A Few-Shot Neural Approach for Layout Analysis of Music Score Images Proceedings Article
In: Proceedings of the 24th International Society for Music Information Retrieval Conference, pp. 106-113, Milan, Italy, 2023, ISBN: 978-1-7327299-3-3.
@inproceedings{Castellanos:2023:few,
title = {A Few-Shot Neural Approach for Layout Analysis of Music Score Images},
author = {F. J. Castellanos, A. J. Gallego, I. Fujinaga},
url = {https://archives.ismir.net/ismir2023/paper/000011.pdf},
isbn = {978-1-7327299-3-3},
year = {2023},
date = {2023-11-05},
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A. J. Gallego F. J. Castellanos, I. Fujinaga
A Preliminary Study of Few-shot Learning for Layout Analysis of Music Scores Working paper
2023.
Abstract | Links | BibTeX | Tags: DOREMI
@workingpaper{Castellanos:2023:preFew,
title = {A Preliminary Study of Few-shot Learning for Layout Analysis of Music Scores},
author = {F. J. Castellanos, A. J. Gallego, I. Fujinaga},
url = {https://arxiv.org/pdf/2311.04091#page=45},
year = {2023},
date = {2023-11-04},
urldate = {2023-11-04},
issue = {WORMS},
abstract = {Proceedings of the 5th International Workshop on Reading Music Systems, Milan, Italy},
keywords = {DOREMI},
pubstate = {published},
tppubtype = {workingpaper}
}
Proceedings of the 5th International Workshop on Reading Music Systems, Milan, Italy
Penarrubia, C.; Valero-Mas, J. J.; Gallego, A. J.; Calvo-Zaragoza, J.
Addressing Class Imbalance in Multilabel Prototype Generation for k-Nearest Neighbor Classification Conference
Iberian Conference on Pattern Recognition and Image Analysis, Alicante, Spain, 2023, ISBN: 978-3-031-36616-1.
Abstract | Links | BibTeX | Tags: DOREMI
@conference{nokey,
title = {Addressing Class Imbalance in Multilabel Prototype Generation for k-Nearest Neighbor Classification},
author = {C. Penarrubia and J. J. Valero-Mas and A. J. Gallego and J. Calvo-Zaragoza},
doi = {https://doi.org/10.1007/978-3-031-36616-1_2},
isbn = {978-3-031-36616-1},
year = {2023},
date = {2023-06-25},
booktitle = {Iberian Conference on Pattern Recognition and Image Analysis},
pages = {15.27},
address = {Alicante, Spain},
abstract = {Prototype Generation (PG) methods seek to improve the efficiency of the k-Nearest Neighbor (kNN) classifier by obtaining a reduced version of a given reference dataset following certain heuristics. Despite being largely addressed topic in multiclass scenarios, few works deal with PG in multilabel environments. Hence, the existing proposals exhibit a number of limitations, being label imbalance one of paramount relevance as it constitutes a typical challenge of multilabel datasets. This work proposes two novel merging policies for multilabel PG schemes specifically devised for label imbalance, as well as a mechanism to prevent inappropriate samples from undergoing a reduction process. These proposals are applied to three existing multilabel PG methods—Multilabel Reduction through Homogeneous Clustering, Multilabel Chen, and Multilabel Reduction through Space Partitioning—and evaluated on 12 different data assortments with different degrees of label imbalance. The results prove that the proposals overcome—in some cases in a significant manner—those obtained with the original methods, hence validating the presented approaches and enabling further research lines on this topic.},
keywords = {DOREMI},
pubstate = {published},
tppubtype = {conference}
}
Prototype Generation (PG) methods seek to improve the efficiency of the k-Nearest Neighbor (kNN) classifier by obtaining a reduced version of a given reference dataset following certain heuristics. Despite being largely addressed topic in multiclass scenarios, few works deal with PG in multilabel environments. Hence, the existing proposals exhibit a number of limitations, being label imbalance one of paramount relevance as it constitutes a typical challenge of multilabel datasets. This work proposes two novel merging policies for multilabel PG schemes specifically devised for label imbalance, as well as a mechanism to prevent inappropriate samples from undergoing a reduction process. These proposals are applied to three existing multilabel PG methods—Multilabel Reduction through Homogeneous Clustering, Multilabel Chen, and Multilabel Reduction through Space Partitioning—and evaluated on 12 different data assortments with different degrees of label imbalance. The results prove that the proposals overcome—in some cases in a significant manner—those obtained with the original methods, hence validating the presented approaches and enabling further research lines on this topic.
F. J. Castellanos A. Rosello, J. P. Martinez-Esteso
Test-Time Augmentation for Document Image Binarization Proceedings Article
In: Iberian Conference on Pattern Recognition and Image Analysis, pp. 158-169, Springer Nature Switzerland, 2023, ISBN: 978-3-031-36615-4.
@inproceedings{Rosello2023:test-time,
title = {Test-Time Augmentation for Document Image Binarization},
author = {A. Rosello, F. J. Castellanos, J. P. Martinez-Esteso, A. J. Gallego, J. Calvo-Zaragoza},
doi = {https://doi.org/10.1007/978-3-031-36616-1_13},
isbn = {978-3-031-36615-4},
year = {2023},
date = {2023-06-25},
urldate = {2023-06-25},
booktitle = {Iberian Conference on Pattern Recognition and Image Analysis},
volume = {14062},
pages = {158-169},
publisher = {Springer Nature Switzerland},
keywords = {DOREMI},
pubstate = {published},
tppubtype = {inproceedings}
}
Valero-Mas, J. J.; Gallego, A. J.; Alonso-Jiménez, P.; Serra, X.
Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification Journal Article
In: Pattern Recognition, vol. 135, pp. 109190, 2023, ISSN: 0031-3203.
Abstract | Links | BibTeX | Tags: DOREMI, MultiScore
@article{k519,
title = {Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification},
author = {J. J. Valero-Mas and A. J. Gallego and P. Alonso-Jiménez and X. Serra},
doi = {https://doi.org/10.1016/j.patcog.2022.109190},
issn = {0031-3203},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Pattern Recognition},
volume = {135},
pages = {109190},
abstract = {Prototype Generation (PG) methods are typically considered for improving the efficiency of the k-Nearest Neighbour (kNN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel kNN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving—both in terms of efficiency and classification performance—the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.},
keywords = {DOREMI, MultiScore},
pubstate = {published},
tppubtype = {article}
}
Prototype Generation (PG) methods are typically considered for improving the efficiency of the k-Nearest Neighbour (kNN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel kNN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving—both in terms of efficiency and classification performance—the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.