Classification of Boar Sperm Head Images using Learning Vector Quantization

Michael Biehl; Piter Pasma; Marten Pijl; Lidia Sánchez; Nicolai Petkov

Classification of Boar Sperm Head Images using Learning Vector Quantization

Michael Biehl, Piter Pasma, Marten Pijl, Lidia Sánchez, Nicolai Petkov

Intelligent Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

140 Downloads (Pure)

Abstract

We apply Learning Vector Quantization (LVQ) in automated boar semen quality assessment. The classification of single boar sperm heads into healthy (normal) and non-normal ones is based on grey-scale microscopic images only. Sample data was classified by veterinary experts and is used for training a system with a number of prototypes for each class. We apply as training schemes Kohonen’s LVQ1 and the variants Generalized LVQ (GLVQ) and Generalized Relevance LVQ (GRLVQ). We compare their performance and study the influence of the employed metric.

Original language	English
Title of host publication	Proc. European Symposium on Artificial Neural Networks
Subtitle of host publication	ESANN 2006
Editors	Michel Verleysen
Publisher	d-side publishing
Number of pages	6
Publication status	Published - 2006

Access to Document

2006ProcESANNBiehl.pdfFinal publisher's version, 117 KB

Handle.net

Persistent link

Cite this

@inproceedings{2c2246bd83674909a17f1a0592dfd459,

title = "Classification of Boar Sperm Head Images using Learning Vector Quantization",

abstract = "We apply Learning Vector Quantization (LVQ) in automated boar semen quality assessment. The classification of single boar sperm heads into healthy (normal) and non-normal ones is based on grey-scale microscopic images only. Sample data was classified by veterinary experts and is used for training a system with a number of prototypes for each class. We apply as training schemes Kohonen{\textquoteright}s LVQ1 and the variants Generalized LVQ (GLVQ) and Generalized Relevance LVQ (GRLVQ). We compare their performance and study the influence of the employed metric.",

author = "Michael Biehl and Piter Pasma and Marten Pijl and Lidia S{\'a}nchez and Nicolai Petkov",

note = "Relation: http://www.rug.nl/informatica/organisatie/overorganisatie/iwi Rights: University of Groningen. Research Institute for Mathematics and Computing Science (IWI)",

year = "2006",

language = "English",

editor = "Michel Verleysen",

booktitle = "Proc. European Symposium on Artificial Neural Networks",

publisher = "d-side publishing",

}

TY - GEN

T1 - Classification of Boar Sperm Head Images using Learning Vector Quantization

AU - Biehl, Michael

AU - Pasma, Piter

AU - Pijl, Marten

AU - Sánchez, Lidia

AU - Petkov, Nicolai

N1 - Relation: http://www.rug.nl/informatica/organisatie/overorganisatie/iwi Rights: University of Groningen. Research Institute for Mathematics and Computing Science (IWI)

PY - 2006

Y1 - 2006

N2 - We apply Learning Vector Quantization (LVQ) in automated boar semen quality assessment. The classification of single boar sperm heads into healthy (normal) and non-normal ones is based on grey-scale microscopic images only. Sample data was classified by veterinary experts and is used for training a system with a number of prototypes for each class. We apply as training schemes Kohonen’s LVQ1 and the variants Generalized LVQ (GLVQ) and Generalized Relevance LVQ (GRLVQ). We compare their performance and study the influence of the employed metric.

AB - We apply Learning Vector Quantization (LVQ) in automated boar semen quality assessment. The classification of single boar sperm heads into healthy (normal) and non-normal ones is based on grey-scale microscopic images only. Sample data was classified by veterinary experts and is used for training a system with a number of prototypes for each class. We apply as training schemes Kohonen’s LVQ1 and the variants Generalized LVQ (GLVQ) and Generalized Relevance LVQ (GRLVQ). We compare their performance and study the influence of the employed metric.

M3 - Conference contribution

BT - Proc. European Symposium on Artificial Neural Networks

A2 - Verleysen, Michel

PB - d-side publishing

ER -

Classification of Boar Sperm Head Images using Learning Vector Quantization

Abstract

Access to Document

Handle.net

Fingerprint

Cite this