Comparison of supervised and unsupervised methods to classify boar acrosomes using texture descriptors

Enrique Alegre*, Victor González-Castro, Sir Suárez, Manuel Castejón

*Corresponding author for this work

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


This work compares supervised and unsupervised techniques to classify images of boar sperm heads according to their membrane integrity. We have used 5 different descriptors to characterize the texture of the acrosomes: Laws method, Legendre moments, Zernike moments and 4 and 13 of the features proposed by Haralick extracted from the co-occurrence matrix. We have carried out the classification using Fisher Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), k-Nearest Neighbours and Backpropagation Neural Networks to classify them. Results show that unsupervised classification methods have better performance than supervised ones: The former yield a best error rate of 6.11%, while the latter achieved a best error rate of about 9%.

Original languageEnglish
Title of host publicationProceedings Elmar - International Symposium Electronics in Marine
Number of pages6
Publication statusPublished - 1 Dec 2009
Externally publishedYes
EventELMAR-2009 - 51st International Symposium ELMAR-2009 - Zadar, United Kingdom
Duration: 28 Sep 200930 Sep 2009


ConferenceELMAR-2009 - 51st International Symposium ELMAR-2009
Country/TerritoryUnited Kingdom


  • Boar semen
  • Co-occurrence matrix
  • Laws
  • Legendre
  • Supervised classification
  • Unsupervised classification
  • Zernike


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