Classification of Boar Sperm Head Images using Learning Vector Quantization

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)
105 Downloads (Pure)


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 languageEnglish
Title of host publicationProc. European Symposium on Artificial Neural Networks
Subtitle of host publicationESANN 2006
EditorsMichel Verleysen
Publisherd-side publishing
Number of pages6
Publication statusPublished - 2006

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