Publications

@article{fidalgo_early_2019,

title = {Early fusion of multi-level saliency descriptors for image classification},

author = {Eduardo Fidalgo and Enrique Alegre and Laura Fernández-Robles and Víctor González-Castro},

url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=opCbArQAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=opCbArQAAAAJ:BrmTIyaxlBUC},

year  = {2019},

date = {2019-01-01},

journal = {Revista Iberoamericana de Automática e Informática industrial},

volume = {16},

number = {3},

pages = {358–368},

abstract = {This paper proposes an improved image classification method by enhancing Bag of Visual Words (BoVW) coding through saliency maps. By treating saliency maps as topographic maps and filtering background features, classification accuracy is improved. Six saliency algorithms were evaluated, selecting GBVS and SIM for retaining object information. SIFT descriptors from the background were filtered using binary images at different saliency levels, and early fusion of these descriptors was tested across five datasets.},

note = {Publisher: UNIV POLITECNICA VALENCIA, EDITORIAL UPV CAMINO VERA SN, VALENCIA, 46022, SPAIN},

keywords = {Bag of Visual Words, BoVW, Feature Filtering, Image classification, Saliency Maps, SIFT Descriptors},

pubstate = {published},

tppubtype = {article}

}

@article{fidalgo_illegal_2018,

title = {Illegal activity categorisation in DarkNet based on image classification using CREIC method},

author = {Eduardo Fidalgo and Enrique Alegre and Victor González-Castro and Laura Fernández-Robles},

url = {https://link.springer.com/chapter/10.1007/978-3-319-67180-2_58},

year  = {2018},

date = {2018-01-01},

journal = {International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding 12},

pages = {600–609},

abstract = {This paper introduces TOIC (TOr Image Categories), a dataset of illegal images from the TOR network, and presents a method to classify them using a combination of Edge-SIFT and dense SIFT descriptors. These features are extracted from edge images created with the Compass Operator. The method employs a Bag of Visual Words model that fuses these descriptors early in the process to effectively detect and categorize illegal content. By selecting the optimal radius before calculating Edge-SIFT, the approach improves classification performance, achieving an accuracy of 92.49% on the TOIC dataset, and showing increased accuracy in tests on both TOIC and the Butterflies dataset. The method offers an efficient tool for identifying illegal content in the TOR network.},

note = {Publisher: Springer International Publishing},

keywords = {Bag of Visual Words, Edge-SIFT descriptors, Image classification, support vector machine, TOR},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_reliability_2017,

title = {Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance},

author = {Víctor González-Castro and María del Carmen Valdés-Hernández and Francesca M Chappell and Paul A Armitage and Stephen Makin and Joanna M Wardlaw},

url = {https://portlandpress.com/clinsci/article/131/13/1465/71656/Reliability-of-an-automatic-classifier-for-brain},

year  = {2017},

date = {2017-01-01},

journal = {Clinical Science},

volume = {131},

number = {13},

pages = {1465–1481},

abstract = {Enlarged perivascular spaces (PVS) in the brain are associated with small vessel disease, poor cognition, and hypertension. This study proposes a fully automated method using a support vector machine (SVM) to classify PVS burden in the basal ganglia (BG) as low or high from T2-weighted MRI images. Three feature extraction techniques were evaluated, with the bag of visual words (BoW) approach achieving the highest accuracy (81.16%). The classifier's performance was comparable to that of trained human observers, and its predictions were clinically meaningful, as indicated by high AUC values (0.90–0.93). These findings suggest that automated PVS burden assessment could serve as a valuable clinical tool.},

note = {Publisher: Portland Press Ltd.},

keywords = {Bag of Visual Words, brain MRI, Discrete Wavelet Transform, Local Binary Patterns, machine learning, perivascular spaces, small vessel disease, support vector machine},

pubstate = {published},

tppubtype = {article}

}

@article{fidalgo_selection_2017-1,

title = {Selection of relevant information to improve image classification using Bag of Visual Words= Selección de información significativa para mejorar la clasificación de imágenes utilizando técnicas de Bag of Visual Words},

author = {Eduardo Fidalgo},

url = {https://buleria.unileon.es/handle/10612/6016},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

abstract = {Esta tesis propone mejorar la clasificación de imágenes utilizando el método Bag of Words (BoW) mediante la combinación de múltiples mapas de atención. Cada mapa contiene "rebanadas de información" a diferentes alturas, que afectan la clasificación. Al combinar estas rebanadas, se obtiene mejor precisión que usándolas por separado, aunque agregar más rebanadas no siempre mejora el rendimiento.},

keywords = {Bag of Visual Words, Clasificación de Imágenes, Mapas de atención, Optimización de características},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_clasificacion_2016,

title = {Clasificación de imágenes con Bag of Visual Words},

author = {Víctor González-Castro and Enrique Alegre and Eduardo Fidalgo},

url = {https://buleria.unileon.es/handle/10612/11032},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

abstract = {La clasificación de imágenes permite a los ordenadores identificar qué contenidos o objetos están presentes en una imagen. El modelo Bag of Visual Words (BoVW) es uno de los más utilizados para esta tarea. BoVW consta de varias etapas: muestreo de puntos característicos, descripción de los mismos, creación de un diccionario de palabras visuales mediante agrupamiento, representación global de las imágenes y, finalmente, clasificación para determinar la clase de la imagen. Este capítulo explica detalladamente el modelo BoVW.},

note = {Publisher: Grupo de Visión del Comité Español de Automática (CEA)},

keywords = {Bag of Visual Words, Clasificación de Imágenes, Imágenes},

pubstate = {published},

tppubtype = {article}

}