Publications
2012
1.
García-Ordás, Maite; Fernández-Robles, Laura; Olivera, Óscar García-Olalla; García-Ordás, Diego; Alegre, Enrique
Boar spermatozoa classication using local invariant features and bag ofwords Artículo de revista
En: Actas de las XXXIII Jornadas de Automática: Vigo, 5 al 7 de Septiembre de 2012, pp. 124, 2012, (Publisher: Universidade de Vigo).
Resumen | Enlaces | BibTeX | Etiquetas: Bag of Words, Image classification, Invariant Local Features, SVM
@article{garcia-ordas_boar_2012,
title = {Boar spermatozoa classication using local invariant features and bag ofwords},
author = {Maite García-Ordás and Laura Fernández-Robles and Óscar García-Olalla Olivera and Diego García-Ordás and Enrique Alegre},
url = {https://d1wqtxts1xzle7.cloudfront.net/44449820/Boar_spermatozoa_classification_using_lo20160405-12762-rpptl6-libre.pdf?1459894371=&response-content-disposition=inline%3B+filename%3DBoar_spermatozoa_classification_using_lo.pdf&Expires=1739810149&Signature=TVTQuev93pbuKg4OlXk4suOi~Coac8HAB8rlkx~gQU1hgQGzVLSHM-qPjGgmrebUZtRI6cO92VmqX5nLYwJZXXqabj7XL~MZdxEyfZFsXefB2yEW47E37QamibGNRwNQOYYXsLMkBcV4yjY0~fk4eEh3muwznGtFmBzYynLuFUsE6eDRmhg3caXHnwOE3ulYfilE-VRBGlORpq-q7c6UMS8rsvmj9L1PvOjwno77xYKcgu5Hf0wOgFkKgIZx-XLJ39pzh2pxzdiiLz7Ghnwmre5XjiqAR6wheQfEey~tzH31B5aewIWwQfYy4FAniapRIv2t~8i9uanYGwwG0ZMu4w__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
year = {2012},
date = {2012-01-01},
journal = {Actas de las XXXIII Jornadas de Automática: Vigo, 5 al 7 de Septiembre de 2012},
pages = {124},
abstract = {In this work, different descriptors and classifiers were compared to classify boar spermatozoa acrosome as intact or damaged using the Bag of Words (BOW) method. This approach models images using a dictionary-based technique, where each image is described by local points from the dictionary without considering spatial information. The method was tested with SVM, kNN, QDA, and LDA classifiers. The dictionary was created using two approaches: k-means and fuzzy clustering. Better results were obtained with the k-means algorithm and SVM classification. Two local invariant descriptors were tested: SIFT with a success rate of 64.88% and SURF with a success rate of 71.75%.},
note = {Publisher: Universidade de Vigo},
keywords = {Bag of Words, Image classification, Invariant Local Features, SVM},
pubstate = {published},
tppubtype = {article}
}
In this work, different descriptors and classifiers were compared to classify boar spermatozoa acrosome as intact or damaged using the Bag of Words (BOW) method. This approach models images using a dictionary-based technique, where each image is described by local points from the dictionary without considering spatial information. The method was tested with SVM, kNN, QDA, and LDA classifiers. The dictionary was created using two approaches: k-means and fuzzy clustering. Better results were obtained with the k-means algorithm and SVM classification. Two local invariant descriptors were tested: SIFT with a success rate of 64.88% and SURF with a success rate of 71.75%.
0000
2.
Fernández-Robles, Laura; Olivera, Óscar García-Olalla; García-Ordás, María Teresa; García-Ordás, Diego; Alegre, Enrique
SVM APPROACH TO CLASSIFY BOAR ACROSOME INTEGRITY OF A MULTI-FEATURES SURF Artículo de revista
En: 0000.
Resumen | Enlaces | BibTeX | Etiquetas: Image classification, Image Recognition, Invariant Local Features, support vector machine, SURF
@article{fernandez-robles_svm_nodate,
title = {SVM APPROACH TO CLASSIFY BOAR ACROSOME INTEGRITY OF A MULTI-FEATURES SURF},
author = {Laura Fernández-Robles and Óscar García-Olalla Olivera and María Teresa García-Ordás and Diego García-Ordás and Enrique Alegre},
url = {https://d1wqtxts1xzle7.cloudfront.net/44449818/SVM_approach_to_classify_boar_acrosome_i20160405-28158-qszl8d-libre.pdf?1459894369=&response-content-disposition=inline%3B+filename%3DSVM_Approach_to_Classify_Boar_Acrosome_I.pdf&Expires=1739795517&Signature=Tgnu3YoKmzyQiRloeYT95Z4ufJAMUtL~2z~sVtWh4x0OwtjsDwwxq7cUYjl-q5NxrhAJJNz3b7f7YchGOHb6p7lf48EUqtmL1Cjm1mI6YY59k3-ds8J53mCRa0SdXtjjZa0MvchGa2Aqbqx3pt5Ep6v5To7Trx3aKfElmzjdaSP7yKZxPa~b92YaH02HFDTQkx8UFEf6TuCoitK-mz4On4xw-6-RfHwdh37FtKePaXdKxv~sHwmvwVWlOn~yaNIPTO1sl3X8LT9zuUU~8yHltm8xUlFuOzWXwgAe8bMmYMWr6HwY-GG7ExpJQj43FmIa6XXflt7MlJRIuAzSgSo~Lw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
abstract = {This paper presents an approach to improve the classification of invariant local feature descriptors in images of boar spermatozoa heads using Support Vector Machine (SVM). The method involves detecting interest points with SURF and classifying the acrosome as intact or damaged. The approach focuses on classifying the whole head rather than individual points, leveraging the fact that a head typically has more distinctive points of its own class than doubtful ones. The results show a hit rate of 90.91%, indicating that this method could be an effective alternative for classifying invariant local features.},
keywords = {Image classification, Image Recognition, Invariant Local Features, support vector machine, SURF},
pubstate = {published},
tppubtype = {article}
}
This paper presents an approach to improve the classification of invariant local feature descriptors in images of boar spermatozoa heads using Support Vector Machine (SVM). The method involves detecting interest points with SURF and classifying the acrosome as intact or damaged. The approach focuses on classifying the whole head rather than individual points, leveraging the fact that a head typically has more distinctive points of its own class than doubtful ones. The results show a hit rate of 90.91%, indicating that this method could be an effective alternative for classifying invariant local features.