Texture classification using kernel-based techniques

Carlos Fernandez-Lozano; Jose A. Seoane; Marcos Gestal; Tom R. Gaunt; Colin Campbell

doi:10.1007/978-3-642-38679-4_42

Texture classification using kernel-based techniques

Carlos Fernandez-Lozano, Jose A. Seoane, Marcos Gestal, Tom R. Gaunt, Colin Campbell

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

4 Citations (Scopus)

Abstract

In this paper, a high-dimensional textural heterogenous dataset is evaluated. This problem should be studied with specific techniques or a solution for decreasing dimensionality should be applied in order to improve the classification results. Thus, this problem is tackled by means of three differente techniques: an specific technique such as Multiple Kernel Learning, and two different feature selection techniques such as Support Vector Machines-Recursive Feature Elimination and a Genetic Algorithm-based approaches. We found that the best technique is Support Vector Machines-Recursive Feature Elimination, with a AUROC score of 92,45%.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	427-434
Number of pages	8
Volume	7902 LNCS
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-38679-4_42
Publication status	Published - 17 Jul 2013
Event	12th International Work-Conference on Artificial Neural Networks, IWANN 2013 - Puerto de la Cruz, Tenerife, United Kingdom Duration: 12 Jun 2013 → 14 Jun 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7902 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	12th International Work-Conference on Artificial Neural Networks, IWANN 2013
Country/Territory	United Kingdom
City	Puerto de la Cruz, Tenerife
Period	12/06/13 → 14/06/13

Keywords

Genetic Algorithms
Multiple Kernel Learning
Recursive Feature Elimination
Support Vector Machines

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10.1007/978-3-642-38679-4_42

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Fernandez-Lozano, C., Seoane, J. A., Gestal, M., Gaunt, T. R., & Campbell, C. (2013). Texture classification using kernel-based techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 7902 LNCS, pp. 427-434). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7902 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-38679-4_42

Fernandez-Lozano, Carlos ; Seoane, Jose A. ; Gestal, Marcos et al. / Texture classification using kernel-based techniques. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7902 LNCS PART 1. ed. 2013. pp. 427-434 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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Fernandez-Lozano, C, Seoane, JA, Gestal, M, Gaunt, TR & Campbell, C 2013, Texture classification using kernel-based techniques. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 7902 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7902 LNCS, pp. 427-434, 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, United Kingdom, 12/06/13. https://doi.org/10.1007/978-3-642-38679-4_42

Texture classification using kernel-based techniques. / Fernandez-Lozano, Carlos; Seoane, Jose A.; Gestal, Marcos et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7902 LNCS PART 1. ed. 2013. p. 427-434 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7902 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AU - Fernandez-Lozano, Carlos

AU - Seoane, Jose A.

AU - Gestal, Marcos

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AU - Campbell, Colin

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AB - In this paper, a high-dimensional textural heterogenous dataset is evaluated. This problem should be studied with specific techniques or a solution for decreasing dimensionality should be applied in order to improve the classification results. Thus, this problem is tackled by means of three differente techniques: an specific technique such as Multiple Kernel Learning, and two different feature selection techniques such as Support Vector Machines-Recursive Feature Elimination and a Genetic Algorithm-based approaches. We found that the best technique is Support Vector Machines-Recursive Feature Elimination, with a AUROC score of 92,45%.

KW - Genetic Algorithms

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Fernandez-Lozano C, Seoane JA, Gestal M, Gaunt TR, Campbell C. Texture classification using kernel-based techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 7902 LNCS. 2013. p. 427-434. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-38679-4_42

Texture classification using kernel-based techniques

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