Classification of Boar Sperm Head Images using Learning Vector Quantization

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.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Michael Biehl, Piter Pasma, Marten Pijl, Lidia Sánchez, Nicolai Petkov
• Keywords: Boar Semen Quality, ESANN 2006, ESANN 2007, Learning Vector Quantization, Single Boar Sperm