2009
Oncina, J.
Optimum Algorithm to Minimize Human Interactions in Sequential Computer Assisted Pattern Recognition Journal Article
In: Pattern Recognition Letters, vol. 30, no. 6, pp. 558-563, 2009, ISSN: 0167-8655.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k226,
title = {Optimum Algorithm to Minimize Human Interactions in Sequential Computer Assisted Pattern Recognition},
author = {J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/226/paper.pdf},
issn = {0167-8655},
year = {2009},
date = {2009-02-01},
journal = {Pattern Recognition Letters},
volume = {30},
number = {6},
pages = {558-563},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Abreu, J.; Rico-Juan, J. R.
Contour regularity extraction based on string edit distance Journal Article
In: Lecture Notes in Computer Science, vol. 5524, pp. 160-167, 2009, ISBN: 0302-9743.
Abstract | BibTeX | Tags: ARFAI, MIPRCV
@article{k228,
title = {Contour regularity extraction based on string edit distance},
author = {J. Abreu and J. R. Rico-Juan},
isbn = {0302-9743},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {Pattern Recognition and Image Analysis. IbPRIA 2009},
journal = {Lecture Notes in Computer Science},
volume = {5524},
pages = {160-167},
publisher = {Springer},
address = {Pòvoa de Varzim, Portugal},
abstract = {In this paper, we present a new method for constructing prototypes representing a set of contours encoded by Freeman Chain Codes.Our method build new prototypes taking into account similar segments shared between contours instances. The similarity criterion was based on the Levenshtein Edit Distance definition. We also outline how to apply our method to reduce a data set without sensibly affect its representational power for classification purposes. Experimental results shows that our scheme can achieve compressions about 50% while classification error increases only by 0.75%.},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a new method for constructing prototypes representing a set of contours encoded by Freeman Chain Codes.Our method build new prototypes taking into account similar segments shared between contours instances. The similarity criterion was based on the Levenshtein Edit Distance definition. We also outline how to apply our method to reduce a data set without sensibly affect its representational power for classification purposes. Experimental results shows that our scheme can achieve compressions about 50% while classification error increases only by 0.75%. Micó, L.; Oncina, J.
Experimental Analysis of Insertion Costs in a Naïve Dynamic MDF-Tree Journal Article
In: Lecture Notes in Computer Science, vol. 5524, pp. 402-408, 2009, ISBN: 978-3-642-02171-8.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k230,
title = {Experimental Analysis of Insertion Costs in a Naïve Dynamic MDF-Tree},
author = {L. Micó and J. Oncina},
editor = {Armando J. Pinho Ana Maria Mendonça Helder Araújo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/230/ibpria09.pdf},
isbn = {978-3-642-02171-8},
year = {2009},
date = {2009-01-01},
booktitle = {Pattern Recognition and Image Analysis},
journal = {Lecture Notes in Computer Science},
volume = {5524},
pages = {402-408},
publisher = {LNCS 5524},
address = {Povoa do Varzim},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Calera-Rubio, J.; Bernabeu, J. F.
A probabilistic approach to melodic similarity Proceedings Article
In: Proceedings of MML 2009, pp. 48-53, 2009.
Abstract | Links | BibTeX | Tags: ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA
@inproceedings{k231,
title = {A probabilistic approach to melodic similarity},
author = {J. Calera-Rubio and J. F. Bernabeu},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/231/mml2009Bernabeu.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {Proceedings of MML 2009},
pages = {48-53},
abstract = {Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.},
keywords = {ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance. Gallego-Sánchez, J.; Calera-Rubio, J.
Improving edge detection in highly noised sheet-metal images Journal Article
In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 43-48, 2009, ISSN: 1550-5790.
Abstract | Links | BibTeX | Tags: ARFAI
@article{k239,
title = {Improving edge detection in highly noised sheet-metal images},
author = {J. Gallego-Sánchez and J. Calera-Rubio},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/239/010.pdf},
issn = {1550-5790},
year = {2009},
date = {2009-01-01},
journal = {IEEE Workshop on Applications of Computer Vision (WACV)},
pages = {43-48},
abstract = {This article proposes a new method for robust and accurate detection of the orientation and the location of an object on low-contrast surfaces in an industrial context. To be more efficient and effective, our method employs only artificial vision. Therefore, productivity is increased since it avoids the use of additional mechanical devices to ensure the accuracy of the system.
The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision.},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {article}
}
This article proposes a new method for robust and accurate detection of the orientation and the location of an object on low-contrast surfaces in an industrial context. To be more efficient and effective, our method employs only artificial vision. Therefore, productivity is increased since it avoids the use of additional mechanical devices to ensure the accuracy of the system.
The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision.2008
Gómez-Ballester, E.; Micó, L.; Oncina, J.
A pruning Rule Based on a Distance Sparse Table for Hierarchical Similarity Search Algorithms Journal Article
In: Lecture Notes in Computer Science, vol. 5342, pp. 936-946, 2008.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k220,
title = {A pruning Rule Based on a Distance Sparse Table for Hierarchical Similarity Search Algorithms},
author = {E. Gómez-Ballester and L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/220/spr-table.pdf},
year = {2008},
date = {2008-12-04},
journal = {Lecture Notes in Computer Science},
volume = {5342},
pages = {936-946},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Boyer, L.; Esposito, Y.; Habrard, A.; Oncina, J.; Sebban, M.
SEDiL: Software for Edit Distance Learning Journal Article
In: Lecture Notes in Computer Science. Machine Learning and Knowledge Discovery in Databases, vol. 5212, pp. 672-677, 2008.
Abstract | Links | BibTeX | Tags: ARFAI
@article{k213,
title = {SEDiL: Software for Edit Distance Learning},
author = {L. Boyer and Y. Esposito and A. Habrard and J. Oncina and M. Sebban},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/213/sedil.pdf},
year = {2008},
date = {2008-09-01},
journal = {Lecture Notes in Computer Science. Machine Learning and Knowledge Discovery in Databases},
volume = {5212},
pages = {672-677},
abstract = {In this paper, we present SEDiL, a Software for Edit Distance Learning. SEDiL is an innovative prototype implementation grouping together most of the state of the art methods that aim to automatically learn the parameters of string and tree edit distances.},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present SEDiL, a Software for Edit Distance Learning. SEDiL is an innovative prototype implementation grouping together most of the state of the art methods that aim to automatically learn the parameters of string and tree edit distances. Oncina, J.
Using Multiplicity Automata to Identify Transducer Relations from Membership and Equivalence Queries Journal Article
In: Lecture Notes in Computer Science. Grammatical Inference: Algorithms and Applications, no. 5278, pp. 154-162, 2008.
@article{k216,
title = {Using Multiplicity Automata to Identify Transducer Relations from Membership and Equivalence Queries},
author = {J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/216/traduccion.pdf},
year = {2008},
date = {2008-09-01},
journal = {Lecture Notes in Computer Science. Grammatical Inference: Algorithms and Applications},
number = {5278},
pages = {154-162},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {article}
}
Higuera, C. De La; Micó, L.
A Contextual Normalised Edit Distance Proceedings Article
In: Proc. of the First International Workshop on Similarity Search and Applications, pp. 61-68, IEEE Computer Society, Cancún, Méjico, 2008, ISSN: 978-0-7696-3101-4.
Abstract | BibTeX | Tags: ARFAI
@inproceedings{k204,
title = {A Contextual Normalised Edit Distance},
author = {C. De La Higuera and L. Micó},
issn = {978-0-7696-3101-4},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. of the First International Workshop on Similarity Search and Applications},
journal = {IEEE Computer Society. Proc. of the First International Workshop on Similarity Search and Applications.},
pages = {61-68},
publisher = {IEEE Computer Society},
address = {Cancún, Méjico},
abstract = {In order to better fit a variety of pattern recognition problems over strings, using a normalised version of the edit or Levenshtein distance is considered to be an appropriate approach. The goal of normalisation is to take into account the lengths of the strings. We define a new normalisation, contextual, where each edit operation is divided by the length of the string on which the edit operation takes place. We prove that this contextual edit distance is a metric and that it can be computed through an extension of the usual dynamic programming algorithm for the edit distance. We also provide a fast heuristic which nearly always returns the same result and we show over several experiments that the distance obtains good results in classification tasks and has a low intrinsic dimension in comparison with other normalised edit distances.},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {inproceedings}
}
In order to better fit a variety of pattern recognition problems over strings, using a normalised version of the edit or Levenshtein distance is considered to be an appropriate approach. The goal of normalisation is to take into account the lengths of the strings. We define a new normalisation, contextual, where each edit operation is divided by the length of the string on which the edit operation takes place. We prove that this contextual edit distance is a metric and that it can be computed through an extension of the usual dynamic programming algorithm for the edit distance. We also provide a fast heuristic which nearly always returns the same result and we show over several experiments that the distance obtains good results in classification tasks and has a low intrinsic dimension in comparison with other normalised edit distances. Micó, L.
A point of view on teaching Pattern Recognition Proceedings Article
In: Jacquemont, S.; Higuera, C. De La (Ed.): Proc. of the Teaching Machine Learning Workshop 2008, pp. 43-45, Université Jean Monnet Saint Etienne, France, 2008.
Abstract | BibTeX | Tags: ARFAI
@inproceedings{k205,
title = {A point of view on teaching Pattern Recognition},
author = {L. Micó},
editor = {S. Jacquemont and C. De La Higuera},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. of the Teaching Machine Learning Workshop 2008},
pages = {43-45},
address = {Saint Etienne, France},
organization = {Université Jean Monnet},
abstract = {I describe in this paper a particular analysis about
what is Pattern Recognition as a field and from a teaching point of view. Although Pattern Recognition
has a strong relation with Machine Learning, in the following discussion I will introduce some hints, based in a previous analysis, that could help to understand an approach to a Pattern Recognition teaching program when compared with others of Machine Learning.},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {inproceedings}
}
I describe in this paper a particular analysis about
what is Pattern Recognition as a field and from a teaching point of view. Although Pattern Recognition
has a strong relation with Machine Learning, in the following discussion I will introduce some hints, based in a previous analysis, that could help to understand an approach to a Pattern Recognition teaching program when compared with others of Machine Learning. Socorro, R.; Micó, L.
Use of Structured Pattern Representations for Combining Classifiers Journal Article
In: Lecture Notes in Computer Science, vol. 5342, pp. 821-830, 2008.
@article{k219,
title = {Use of Structured Pattern Representations for Combining Classifiers},
author = {R. Socorro and L. Micó},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/219/spr08.pdf},
year = {2008},
date = {2008-01-01},
journal = {Lecture Notes in Computer Science},
volume = {5342},
pages = {821-830},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {article}
}
Olivares-Rodríguez, C.; Oncina, J.
A Stochastic Approach to Median String Computation Journal Article
In: Lecture Notes in Computer Science, vol. 5342, pp. 431–440, 2008.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k223,
title = {A Stochastic Approach to Median String Computation},
author = {C. Olivares-Rodríguez and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/223/median.pdf},
year = {2008},
date = {2008-01-01},
journal = {Lecture Notes in Computer Science},
volume = {5342},
pages = {431–440},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
2007
Moreno-Seco, F.; Micó, L.; Mazón, J. N.
New neighborhood based classification rules for metric spaces and their use in ensemble classification Journal Article
In: Lecture Notes in Computer Science, vol. 4477, no. to appear, pp. 354-361, 2007.
Abstract | BibTeX | Tags: ARFAI, GV-JRRJ
@article{k192,
title = {New neighborhood based classification rules for metric spaces and their use in ensemble classification},
author = {F. Moreno-Seco and L. Micó and J. N. Mazón},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {Lecture Notes in Computer Science},
volume = {4477},
number = {to appear},
pages = {354-361},
abstract = {The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification
tasks. Based on the neighborhood concept, several
classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification rules derived from them are used in several real data classification tasks. Also, some voting ensembles of classifiers based on these
new rules have been tested and compared.},
keywords = {ARFAI, GV-JRRJ},
pubstate = {published},
tppubtype = {article}
}
The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification
tasks. Based on the neighborhood concept, several
classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification rules derived from them are used in several real data classification tasks. Also, some voting ensembles of classifiers based on these
new rules have been tested and compared. Gómez-Ballester, E.; Thollard, F.; Oncina, J.
A tabular pruning rule in tree-based pruning rule fast nearest neighbour search algorithms Journal Article
In: Lecture Notes in Computer Science, vol. 4478, pp. 306-313, 2007.
Abstract | Links | BibTeX | Tags: ARFAI, GV-JRRJ
@article{k193,
title = {A tabular pruning rule in tree-based pruning rule fast nearest neighbour search algorithms},
author = {E. Gómez-Ballester and F. Thollard and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/193/ibpria-eva-2007.pdf},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {Lecture Notes in Computer Science},
volume = {4478},
pages = {306-313},
abstract = {Some fast nearest neighbor search (NNS) algorithms using metric properties have appeared in the last years for reducing computational cost. Depending on the structure used to store the training set, different strategies to speed up the search have been defined. For instance, pruning rules avoid the search of some branches of a tree in a tree-based search algorithm. In this paper, we propose a new and simple pruning rule that can be used in most of the tree-based search algorithms.
All the information needed by the rule can be stored in a table (at preprocessing time). Moreover, the rule can be computed in constant time. This approach is evaluated through real and artificial data experiments. In order to test its performance, the rule is compared to and combined with other previously defined rules.},
keywords = {ARFAI, GV-JRRJ},
pubstate = {published},
tppubtype = {article}
}
Some fast nearest neighbor search (NNS) algorithms using metric properties have appeared in the last years for reducing computational cost. Depending on the structure used to store the training set, different strategies to speed up the search have been defined. For instance, pruning rules avoid the search of some branches of a tree in a tree-based search algorithm. In this paper, we propose a new and simple pruning rule that can be used in most of the tree-based search algorithms.
All the information needed by the rule can be stored in a table (at preprocessing time). Moreover, the rule can be computed in constant time. This approach is evaluated through real and artificial data experiments. In order to test its performance, the rule is compared to and combined with other previously defined rules. Rico-Juan, J. R.; Iñesta, J. M.
Normalisation of confidence voting methods applied to a fast handwritten OCR classification Journal Article
In: Advances in Soft Computing. Computer Recognition Systems 2, vol. 45, pp. 405-412, 2007.
Links | BibTeX | Tags: ARFAI, GV-JRRJ
@article{k198,
title = {Normalisation of confidence voting methods applied to a fast handwritten OCR classification},
author = {J. R. Rico-Juan and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/198/combinacionClasificadores.pdf},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {Advances in Soft Computing. Computer Recognition Systems 2},
volume = {45},
pages = {405-412},
keywords = {ARFAI, GV-JRRJ},
pubstate = {published},
tppubtype = {article}
}
2009
Oncina, J.
Optimum Algorithm to Minimize Human Interactions in Sequential Computer Assisted Pattern Recognition Journal Article
In: Pattern Recognition Letters, vol. 30, no. 6, pp. 558-563, 2009, ISSN: 0167-8655.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k226,
title = {Optimum Algorithm to Minimize Human Interactions in Sequential Computer Assisted Pattern Recognition},
author = {J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/226/paper.pdf},
issn = {0167-8655},
year = {2009},
date = {2009-02-01},
journal = {Pattern Recognition Letters},
volume = {30},
number = {6},
pages = {558-563},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Abreu, J.; Rico-Juan, J. R.
Contour regularity extraction based on string edit distance Journal Article
In: Lecture Notes in Computer Science, vol. 5524, pp. 160-167, 2009, ISBN: 0302-9743.
Abstract | BibTeX | Tags: ARFAI, MIPRCV
@article{k228,
title = {Contour regularity extraction based on string edit distance},
author = {J. Abreu and J. R. Rico-Juan},
isbn = {0302-9743},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {Pattern Recognition and Image Analysis. IbPRIA 2009},
journal = {Lecture Notes in Computer Science},
volume = {5524},
pages = {160-167},
publisher = {Springer},
address = {Pòvoa de Varzim, Portugal},
abstract = {In this paper, we present a new method for constructing prototypes representing a set of contours encoded by Freeman Chain Codes.Our method build new prototypes taking into account similar segments shared between contours instances. The similarity criterion was based on the Levenshtein Edit Distance definition. We also outline how to apply our method to reduce a data set without sensibly affect its representational power for classification purposes. Experimental results shows that our scheme can achieve compressions about 50% while classification error increases only by 0.75%.},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present a new method for constructing prototypes representing a set of contours encoded by Freeman Chain Codes.Our method build new prototypes taking into account similar segments shared between contours instances. The similarity criterion was based on the Levenshtein Edit Distance definition. We also outline how to apply our method to reduce a data set without sensibly affect its representational power for classification purposes. Experimental results shows that our scheme can achieve compressions about 50% while classification error increases only by 0.75%.
Micó, L.; Oncina, J.
Experimental Analysis of Insertion Costs in a Naïve Dynamic MDF-Tree Journal Article
In: Lecture Notes in Computer Science, vol. 5524, pp. 402-408, 2009, ISBN: 978-3-642-02171-8.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k230,
title = {Experimental Analysis of Insertion Costs in a Naïve Dynamic MDF-Tree},
author = {L. Micó and J. Oncina},
editor = {Armando J. Pinho Ana Maria Mendonça Helder Araújo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/230/ibpria09.pdf},
isbn = {978-3-642-02171-8},
year = {2009},
date = {2009-01-01},
booktitle = {Pattern Recognition and Image Analysis},
journal = {Lecture Notes in Computer Science},
volume = {5524},
pages = {402-408},
publisher = {LNCS 5524},
address = {Povoa do Varzim},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Calera-Rubio, J.; Bernabeu, J. F.
A probabilistic approach to melodic similarity Proceedings Article
In: Proceedings of MML 2009, pp. 48-53, 2009.
Abstract | Links | BibTeX | Tags: ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA
@inproceedings{k231,
title = {A probabilistic approach to melodic similarity},
author = {J. Calera-Rubio and J. F. Bernabeu},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/231/mml2009Bernabeu.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {Proceedings of MML 2009},
pages = {48-53},
abstract = {Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.},
keywords = {ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.
Gallego-Sánchez, J.; Calera-Rubio, J.
Improving edge detection in highly noised sheet-metal images Journal Article
In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 43-48, 2009, ISSN: 1550-5790.
Abstract | Links | BibTeX | Tags: ARFAI
@article{k239,
title = {Improving edge detection in highly noised sheet-metal images},
author = {J. Gallego-Sánchez and J. Calera-Rubio},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/239/010.pdf},
issn = {1550-5790},
year = {2009},
date = {2009-01-01},
journal = {IEEE Workshop on Applications of Computer Vision (WACV)},
pages = {43-48},
abstract = {This article proposes a new method for robust and accurate detection of the orientation and the location of an object on low-contrast surfaces in an industrial context. To be more efficient and effective, our method employs only artificial vision. Therefore, productivity is increased since it avoids the use of additional mechanical devices to ensure the accuracy of the system.
The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision.},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {article}
}
This article proposes a new method for robust and accurate detection of the orientation and the location of an object on low-contrast surfaces in an industrial context. To be more efficient and effective, our method employs only artificial vision. Therefore, productivity is increased since it avoids the use of additional mechanical devices to ensure the accuracy of the system.
The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision.
The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision.
2008
Gómez-Ballester, E.; Micó, L.; Oncina, J.
A pruning Rule Based on a Distance Sparse Table for Hierarchical Similarity Search Algorithms Journal Article
In: Lecture Notes in Computer Science, vol. 5342, pp. 936-946, 2008.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k220,
title = {A pruning Rule Based on a Distance Sparse Table for Hierarchical Similarity Search Algorithms},
author = {E. Gómez-Ballester and L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/220/spr-table.pdf},
year = {2008},
date = {2008-12-04},
journal = {Lecture Notes in Computer Science},
volume = {5342},
pages = {936-946},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Boyer, L.; Esposito, Y.; Habrard, A.; Oncina, J.; Sebban, M.
SEDiL: Software for Edit Distance Learning Journal Article
In: Lecture Notes in Computer Science. Machine Learning and Knowledge Discovery in Databases, vol. 5212, pp. 672-677, 2008.
Abstract | Links | BibTeX | Tags: ARFAI
@article{k213,
title = {SEDiL: Software for Edit Distance Learning},
author = {L. Boyer and Y. Esposito and A. Habrard and J. Oncina and M. Sebban},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/213/sedil.pdf},
year = {2008},
date = {2008-09-01},
journal = {Lecture Notes in Computer Science. Machine Learning and Knowledge Discovery in Databases},
volume = {5212},
pages = {672-677},
abstract = {In this paper, we present SEDiL, a Software for Edit Distance Learning. SEDiL is an innovative prototype implementation grouping together most of the state of the art methods that aim to automatically learn the parameters of string and tree edit distances.},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present SEDiL, a Software for Edit Distance Learning. SEDiL is an innovative prototype implementation grouping together most of the state of the art methods that aim to automatically learn the parameters of string and tree edit distances.
Oncina, J.
Using Multiplicity Automata to Identify Transducer Relations from Membership and Equivalence Queries Journal Article
In: Lecture Notes in Computer Science. Grammatical Inference: Algorithms and Applications, no. 5278, pp. 154-162, 2008.
@article{k216,
title = {Using Multiplicity Automata to Identify Transducer Relations from Membership and Equivalence Queries},
author = {J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/216/traduccion.pdf},
year = {2008},
date = {2008-09-01},
journal = {Lecture Notes in Computer Science. Grammatical Inference: Algorithms and Applications},
number = {5278},
pages = {154-162},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {article}
}
Higuera, C. De La; Micó, L.
A Contextual Normalised Edit Distance Proceedings Article
In: Proc. of the First International Workshop on Similarity Search and Applications, pp. 61-68, IEEE Computer Society, Cancún, Méjico, 2008, ISSN: 978-0-7696-3101-4.
Abstract | BibTeX | Tags: ARFAI
@inproceedings{k204,
title = {A Contextual Normalised Edit Distance},
author = {C. De La Higuera and L. Micó},
issn = {978-0-7696-3101-4},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. of the First International Workshop on Similarity Search and Applications},
journal = {IEEE Computer Society. Proc. of the First International Workshop on Similarity Search and Applications.},
pages = {61-68},
publisher = {IEEE Computer Society},
address = {Cancún, Méjico},
abstract = {In order to better fit a variety of pattern recognition problems over strings, using a normalised version of the edit or Levenshtein distance is considered to be an appropriate approach. The goal of normalisation is to take into account the lengths of the strings. We define a new normalisation, contextual, where each edit operation is divided by the length of the string on which the edit operation takes place. We prove that this contextual edit distance is a metric and that it can be computed through an extension of the usual dynamic programming algorithm for the edit distance. We also provide a fast heuristic which nearly always returns the same result and we show over several experiments that the distance obtains good results in classification tasks and has a low intrinsic dimension in comparison with other normalised edit distances.},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {inproceedings}
}
In order to better fit a variety of pattern recognition problems over strings, using a normalised version of the edit or Levenshtein distance is considered to be an appropriate approach. The goal of normalisation is to take into account the lengths of the strings. We define a new normalisation, contextual, where each edit operation is divided by the length of the string on which the edit operation takes place. We prove that this contextual edit distance is a metric and that it can be computed through an extension of the usual dynamic programming algorithm for the edit distance. We also provide a fast heuristic which nearly always returns the same result and we show over several experiments that the distance obtains good results in classification tasks and has a low intrinsic dimension in comparison with other normalised edit distances.
Micó, L.
A point of view on teaching Pattern Recognition Proceedings Article
In: Jacquemont, S.; Higuera, C. De La (Ed.): Proc. of the Teaching Machine Learning Workshop 2008, pp. 43-45, Université Jean Monnet Saint Etienne, France, 2008.
Abstract | BibTeX | Tags: ARFAI
@inproceedings{k205,
title = {A point of view on teaching Pattern Recognition},
author = {L. Micó},
editor = {S. Jacquemont and C. De La Higuera},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
booktitle = {Proc. of the Teaching Machine Learning Workshop 2008},
pages = {43-45},
address = {Saint Etienne, France},
organization = {Université Jean Monnet},
abstract = {I describe in this paper a particular analysis about
what is Pattern Recognition as a field and from a teaching point of view. Although Pattern Recognition
has a strong relation with Machine Learning, in the following discussion I will introduce some hints, based in a previous analysis, that could help to understand an approach to a Pattern Recognition teaching program when compared with others of Machine Learning.},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {inproceedings}
}
I describe in this paper a particular analysis about
what is Pattern Recognition as a field and from a teaching point of view. Although Pattern Recognition
has a strong relation with Machine Learning, in the following discussion I will introduce some hints, based in a previous analysis, that could help to understand an approach to a Pattern Recognition teaching program when compared with others of Machine Learning.
what is Pattern Recognition as a field and from a teaching point of view. Although Pattern Recognition
has a strong relation with Machine Learning, in the following discussion I will introduce some hints, based in a previous analysis, that could help to understand an approach to a Pattern Recognition teaching program when compared with others of Machine Learning.
Socorro, R.; Micó, L.
Use of Structured Pattern Representations for Combining Classifiers Journal Article
In: Lecture Notes in Computer Science, vol. 5342, pp. 821-830, 2008.
@article{k219,
title = {Use of Structured Pattern Representations for Combining Classifiers},
author = {R. Socorro and L. Micó},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/219/spr08.pdf},
year = {2008},
date = {2008-01-01},
journal = {Lecture Notes in Computer Science},
volume = {5342},
pages = {821-830},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {article}
}
Olivares-Rodríguez, C.; Oncina, J.
A Stochastic Approach to Median String Computation Journal Article
In: Lecture Notes in Computer Science, vol. 5342, pp. 431–440, 2008.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k223,
title = {A Stochastic Approach to Median String Computation},
author = {C. Olivares-Rodríguez and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/223/median.pdf},
year = {2008},
date = {2008-01-01},
journal = {Lecture Notes in Computer Science},
volume = {5342},
pages = {431–440},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
2007
Moreno-Seco, F.; Micó, L.; Mazón, J. N.
New neighborhood based classification rules for metric spaces and their use in ensemble classification Journal Article
In: Lecture Notes in Computer Science, vol. 4477, no. to appear, pp. 354-361, 2007.
Abstract | BibTeX | Tags: ARFAI, GV-JRRJ
@article{k192,
title = {New neighborhood based classification rules for metric spaces and their use in ensemble classification},
author = {F. Moreno-Seco and L. Micó and J. N. Mazón},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {Lecture Notes in Computer Science},
volume = {4477},
number = {to appear},
pages = {354-361},
abstract = {The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification
tasks. Based on the neighborhood concept, several
classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification rules derived from them are used in several real data classification tasks. Also, some voting ensembles of classifiers based on these
new rules have been tested and compared.},
keywords = {ARFAI, GV-JRRJ},
pubstate = {published},
tppubtype = {article}
}
The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification
tasks. Based on the neighborhood concept, several
classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification rules derived from them are used in several real data classification tasks. Also, some voting ensembles of classifiers based on these
new rules have been tested and compared.
tasks. Based on the neighborhood concept, several
classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification rules derived from them are used in several real data classification tasks. Also, some voting ensembles of classifiers based on these
new rules have been tested and compared.
Gómez-Ballester, E.; Thollard, F.; Oncina, J.
A tabular pruning rule in tree-based pruning rule fast nearest neighbour search algorithms Journal Article
In: Lecture Notes in Computer Science, vol. 4478, pp. 306-313, 2007.
Abstract | Links | BibTeX | Tags: ARFAI, GV-JRRJ
@article{k193,
title = {A tabular pruning rule in tree-based pruning rule fast nearest neighbour search algorithms},
author = {E. Gómez-Ballester and F. Thollard and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/193/ibpria-eva-2007.pdf},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {Lecture Notes in Computer Science},
volume = {4478},
pages = {306-313},
abstract = {Some fast nearest neighbor search (NNS) algorithms using metric properties have appeared in the last years for reducing computational cost. Depending on the structure used to store the training set, different strategies to speed up the search have been defined. For instance, pruning rules avoid the search of some branches of a tree in a tree-based search algorithm. In this paper, we propose a new and simple pruning rule that can be used in most of the tree-based search algorithms.
All the information needed by the rule can be stored in a table (at preprocessing time). Moreover, the rule can be computed in constant time. This approach is evaluated through real and artificial data experiments. In order to test its performance, the rule is compared to and combined with other previously defined rules.},
keywords = {ARFAI, GV-JRRJ},
pubstate = {published},
tppubtype = {article}
}
Some fast nearest neighbor search (NNS) algorithms using metric properties have appeared in the last years for reducing computational cost. Depending on the structure used to store the training set, different strategies to speed up the search have been defined. For instance, pruning rules avoid the search of some branches of a tree in a tree-based search algorithm. In this paper, we propose a new and simple pruning rule that can be used in most of the tree-based search algorithms.
All the information needed by the rule can be stored in a table (at preprocessing time). Moreover, the rule can be computed in constant time. This approach is evaluated through real and artificial data experiments. In order to test its performance, the rule is compared to and combined with other previously defined rules.
All the information needed by the rule can be stored in a table (at preprocessing time). Moreover, the rule can be computed in constant time. This approach is evaluated through real and artificial data experiments. In order to test its performance, the rule is compared to and combined with other previously defined rules.
Rico-Juan, J. R.; Iñesta, J. M.
Normalisation of confidence voting methods applied to a fast handwritten OCR classification Journal Article
In: Advances in Soft Computing. Computer Recognition Systems 2, vol. 45, pp. 405-412, 2007.
Links | BibTeX | Tags: ARFAI, GV-JRRJ
@article{k198,
title = {Normalisation of confidence voting methods applied to a fast handwritten OCR classification},
author = {J. R. Rico-Juan and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/198/combinacionClasificadores.pdf},
year = {2007},
date = {2007-01-01},
urldate = {2007-01-01},
journal = {Advances in Soft Computing. Computer Recognition Systems 2},
volume = {45},
pages = {405-412},
keywords = {ARFAI, GV-JRRJ},
pubstate = {published},
tppubtype = {article}
}