Publications
2018
1.
Fidalgo, Eduardo; Alegre, Enrique; González-Castro, Víctor; Fernández-Robles, Laura
Boosting image classification through semantic attention filtering strategies Artículo de revista
En: Pattern Recognition Letters, vol. 112, pp. 176–183, 2018, (Publisher: North-Holland).
Resumen | Enlaces | BibTeX | Etiquetas: Bag of Words, Image classification, Mean Shift, Saliency Map, support vector machine
@article{fidalgo_boosting_2018,
title = {Boosting image classification through semantic attention filtering strategies},
author = {Eduardo Fidalgo and Enrique Alegre and Víctor González-Castro and Laura Fernández-Robles},
url = {https://www.sciencedirect.com/science/article/pii/S0167865518302757},
year = {2018},
date = {2018-01-01},
journal = {Pattern Recognition Letters},
volume = {112},
pages = {176–183},
abstract = {This paper presents three attention filtering methods based on saliency maps to enhance image classification using BoVW, SPM, and CNN features. The proposed strategies include AutoBlur for selecting the image signature's blurring factor and two SARF variants: one using Mean Shift segmentation and the other with a key point voting system. Experiments show that these methods improve classification performance in five datasets, outperforming baseline methods with BoVW, and achieving competitive results with SPM and CNN.},
note = {Publisher: North-Holland},
keywords = {Bag of Words, Image classification, Mean Shift, Saliency Map, support vector machine},
pubstate = {published},
tppubtype = {article}
}
This paper presents three attention filtering methods based on saliency maps to enhance image classification using BoVW, SPM, and CNN features. The proposed strategies include AutoBlur for selecting the image signature's blurring factor and two SARF variants: one using Mean Shift segmentation and the other with a key point voting system. Experiments show that these methods improve classification performance in five datasets, outperforming baseline methods with BoVW, and achieving competitive results with SPM and CNN.
2012
2.
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
3.
Fidalgo, Eduardo; Fernández-Robles, Laura; García-Ordás, Maite; Olivera, Óscar García-Olalla; Alegre, Enrique
EVALUATION OF SHAPE AND COLOR DESCRIPTORS BY USING BAG OF WORD TECHNIQUES WITH ONE VS ALL CLASSIFICATION Artículo de revista
En: 0000.
Resumen | Enlaces | BibTeX | Etiquetas: Bag of Words, Color Descriptors, One-vs-all, Shape Description
@article{fidalgo_evaluation_nodate,
title = {EVALUATION OF SHAPE AND COLOR DESCRIPTORS BY USING BAG OF WORD TECHNIQUES WITH ONE VS ALL CLASSIFICATION},
author = {Eduardo Fidalgo and Laura Fernández-Robles and Maite García-Ordás and Óscar García-Olalla Olivera and Enrique Alegre},
url = {https://www.researchgate.net/profile/Eduardo-Fidalgo-4/publication/257758029_Evaluation_of_shape_and_color_descriptors_by_using_bag_of_Word_techniques_with_one_vs_all_classification/links/5a2032d8458515341c85589a/Evaluation-of-shape-and-color-descriptors-by-using-bag-of-Word-techniques-with-one-vs-all-classification.pdf},
abstract = {This paper evaluates various shape and color descriptors on two datasets: “Soccer” and a newly introduced one called “Karinas_Dataset1.” Images are represented using descriptors created through the Bag of Words (BoW) technique. A one-vs-all classifier is then trained and tested with random images from the datasets. To avoid outliers, experiments are repeated 10 times and the average result is reported.},
keywords = {Bag of Words, Color Descriptors, One-vs-all, Shape Description},
pubstate = {published},
tppubtype = {article}
}
This paper evaluates various shape and color descriptors on two datasets: “Soccer” and a newly introduced one called “Karinas_Dataset1.” Images are represented using descriptors created through the Bag of Words (BoW) technique. A one-vs-all classifier is then trained and tested with random images from the datasets. To avoid outliers, experiments are repeated 10 times and the average result is reported.