Feature space reduction for graph-based image classification

Niusvel Acosta-Mendoza, Andrés Gago-Alonso, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, José E. Medina-Pagola

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Feature selection is an essential preprocessing step for classifiers with high dimensional training sets. In pattern recognition, feature selection improves the performance of classification by reducing the feature space but preserving the classification capabilities of the original feature space. Image classification using frequent approximate subgraph mining (FASM) is an example where the benefits of features selections are needed. This is due using frequent approximate subgraphs (FAS) leads to high dimensional representations. In this paper, we explore the use of feature selection algorithms in order to reduce the representation of an image collection represented through FASs. In our results we report a dimensionality reduction of over 50% of the original features and we get similar classification results than those reported by using all the features. © Springer-Verlag 2013.
Original languageAmerican English
Title of host publicationFeature space reduction for graph-based image classification
Pages246-253
Number of pages220
ISBN (Electronic)9783642418211
DOIs
StatePublished - 1 Dec 2013
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2014 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8258 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/14 → …

Fingerprint

Image classification
Image Classification
Feature Space
Feature Selection
Feature extraction
Graph in graph theory
Subgraph
High-dimensional
Dimensionality Reduction
Pattern Recognition
Pattern recognition
Preprocessing
Mining
Classifiers
Classifier

Cite this

Acosta-Mendoza, N., Gago-Alonso, A., Carrasco-Ochoa, J. A., Martínez-Trinidad, J. F., & Medina-Pagola, J. E. (2013). Feature space reduction for graph-based image classification. In Feature space reduction for graph-based image classification (pp. 246-253). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8258 LNCS). https://doi.org/10.1007/978-3-642-41822-8_31
Acosta-Mendoza, Niusvel ; Gago-Alonso, Andrés ; Carrasco-Ochoa, Jesús Ariel ; Martínez-Trinidad, José Francisco ; Medina-Pagola, José E. / Feature space reduction for graph-based image classification. Feature space reduction for graph-based image classification. 2013. pp. 246-253 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{04087bfc7a7d440388ac9d22500e169b,
title = "Feature space reduction for graph-based image classification",
abstract = "Feature selection is an essential preprocessing step for classifiers with high dimensional training sets. In pattern recognition, feature selection improves the performance of classification by reducing the feature space but preserving the classification capabilities of the original feature space. Image classification using frequent approximate subgraph mining (FASM) is an example where the benefits of features selections are needed. This is due using frequent approximate subgraphs (FAS) leads to high dimensional representations. In this paper, we explore the use of feature selection algorithms in order to reduce the representation of an image collection represented through FASs. In our results we report a dimensionality reduction of over 50{\%} of the original features and we get similar classification results than those reported by using all the features. {\circledC} Springer-Verlag 2013.",
author = "Niusvel Acosta-Mendoza and Andr{\'e}s Gago-Alonso and Carrasco-Ochoa, {Jes{\'u}s Ariel} and Mart{\'i}nez-Trinidad, {Jos{\'e} Francisco} and Medina-Pagola, {Jos{\'e} E.}",
year = "2013",
month = "12",
day = "1",
doi = "10.1007/978-3-642-41822-8_31",
language = "American English",
isbn = "9783642418211",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "246--253",
booktitle = "Feature space reduction for graph-based image classification",

}

Acosta-Mendoza, N, Gago-Alonso, A, Carrasco-Ochoa, JA, Martínez-Trinidad, JF & Medina-Pagola, JE 2013, Feature space reduction for graph-based image classification. in Feature space reduction for graph-based image classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8258 LNCS, pp. 246-253, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1/01/14. https://doi.org/10.1007/978-3-642-41822-8_31

Feature space reduction for graph-based image classification. / Acosta-Mendoza, Niusvel; Gago-Alonso, Andrés; Carrasco-Ochoa, Jesús Ariel; Martínez-Trinidad, José Francisco; Medina-Pagola, José E.

Feature space reduction for graph-based image classification. 2013. p. 246-253 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8258 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Feature space reduction for graph-based image classification

AU - Acosta-Mendoza, Niusvel

AU - Gago-Alonso, Andrés

AU - Carrasco-Ochoa, Jesús Ariel

AU - Martínez-Trinidad, José Francisco

AU - Medina-Pagola, José E.

PY - 2013/12/1

Y1 - 2013/12/1

N2 - Feature selection is an essential preprocessing step for classifiers with high dimensional training sets. In pattern recognition, feature selection improves the performance of classification by reducing the feature space but preserving the classification capabilities of the original feature space. Image classification using frequent approximate subgraph mining (FASM) is an example where the benefits of features selections are needed. This is due using frequent approximate subgraphs (FAS) leads to high dimensional representations. In this paper, we explore the use of feature selection algorithms in order to reduce the representation of an image collection represented through FASs. In our results we report a dimensionality reduction of over 50% of the original features and we get similar classification results than those reported by using all the features. © Springer-Verlag 2013.

AB - Feature selection is an essential preprocessing step for classifiers with high dimensional training sets. In pattern recognition, feature selection improves the performance of classification by reducing the feature space but preserving the classification capabilities of the original feature space. Image classification using frequent approximate subgraph mining (FASM) is an example where the benefits of features selections are needed. This is due using frequent approximate subgraphs (FAS) leads to high dimensional representations. In this paper, we explore the use of feature selection algorithms in order to reduce the representation of an image collection represented through FASs. In our results we report a dimensionality reduction of over 50% of the original features and we get similar classification results than those reported by using all the features. © Springer-Verlag 2013.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84893203804&origin=inward

UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84893203804&origin=inward

U2 - 10.1007/978-3-642-41822-8_31

DO - 10.1007/978-3-642-41822-8_31

M3 - Conference contribution

SN - 9783642418211

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 246

EP - 253

BT - Feature space reduction for graph-based image classification

ER -

Acosta-Mendoza N, Gago-Alonso A, Carrasco-Ochoa JA, Martínez-Trinidad JF, Medina-Pagola JE. Feature space reduction for graph-based image classification. In Feature space reduction for graph-based image classification. 2013. p. 246-253. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-41822-8_31