Search Results for author: Humberto Bustince

Found 15 papers, 7 papers with code

The Concept of Semantic Value in Social Network Analysis: an Application to Comparative Mythology

1 code implementation13 Sep 2021 Javier Fumanal-Idocin, Oscar Cordón, Graçaliz Dimuro, María Minárová, Humberto Bustince

We use these concept of meaning and semantic affinity to analyze and compare the gods and heroes from three different classical mythologies: Greek, Celtic and Nordic.

Neuro-inspired edge feature fusion using Choquet integrals

1 code implementation22 Apr 2021 Cedric Marco-Detchart, Giancarlo Lucca, Carlos Lopez-Molina, Laura De Miguel, Graçaliz Pereira Dimuro, Humberto Bustince

In this work we elaborate on the fusion of early vision primitives using generalizations of the Choquet integral, and novel aggregation operators that have been extensively studied in recent years.

Boundary Detection Edge Detection

A fusion method for multi-valued data

no code implementations25 Jan 2021 Martin Papčo, Iosu Rodríguez-Martínez, Javier Fumanal-Idocin, Abdulrahman H. Altalhi, Humberto Bustince

In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data.

Decision Making

Learning ordered pooling weights in image classification

1 code implementation2 Jul 2020 J. I. Forcen, Miguel Pagola, Edurne Barrenechea, Humberto Bustince

We present a method to learn the weights of the OWA aggregation operator in a Bag-of-Words framework and in Convolutional Neural Networks, and provide an extensive evaluation showing that OWA based pooling outperforms classical aggregation operators.

Classification General Classification +1

Co-occurrence of deep convolutional features for image search

1 code implementation30 Mar 2020 J. I. Forcen, Miguel Pagola, Edurne Barrenechea, Humberto Bustince

The feature map from the last convolutional layer of a CNN encodes descriptive information from which a discriminative global descriptor can be obtained.

Descriptive Image Retrieval +1

Adaptive binarization based on fuzzy integrals

no code implementations4 Mar 2020 Francesco Bardozzo, Borja De La Osa, Lubomira Horanska, Javier Fumanal-Idocin, Mattia delli Priscoli, Luigi Troiano, Roberto Tagliaferri, Javier Fernandez, Humberto Bustince

This document presents a new adaptive binarization technique based on fuzzy integral images through an efficient design of a modified SAT for fuzzy integrals.


Do we still need fuzzy classifiers for Small Data in the Era of Big Data?

no code implementations8 Mar 2019 Mikel Elkano, Humberto Bustince, Mikel Galar

Our findings show that, although slightly inferior, Big Data classifiers are gradually catching up with state-of-the-art classifiers for Small data, suggesting that a unified learning algorithm for Big and Small Data might be possible.

Small Data Image Classification

CFM-BD: a distributed rule induction algorithm for building Compact Fuzzy Models in Big Data classification problems

1 code implementation25 Feb 2019 Mikel Elkano, Jose Sanz, Edurne Barrenechea, Humberto Bustince, Mikel Galar

However, when it comes to Big Data classification problems, fuzzy rule-based classifiers have not been able to maintain the good trade-off between accuracy and interpretability that has characterized these techniques in non-Big Data environments.

General Classification

Generalized Interval-valued OWA Operators with Interval Weights Derived from Interval-valued Overlap Functions

no code implementations20 Oct 2016 Benjamin Bedregal, Humberto Bustince, Eduardo Palmeira, Graçaliz Pereira Dimuro, Javier Fernandez

In this work we extend to the interval-valued setting the notion of an overlap functions and we discuss a method which makes use of interval-valued overlap functions for constructing OWA operators with interval-valued weights.

Optical images-based edge detection in Synthetic Aperture Radar images

no code implementations24 Aug 2015 Gilberto P. Silva Junior, Alejandro C. Frery, Sandra Sandri, Humberto Bustince, Edurne Barrenechea, Cédric Marco-Detchart

We compare the modified and unmodified versions of the gravitational edge detection technique with the well-established one proposed by Canny, as well as with a recent multiscale fuzzy-based technique proposed by Lopez-Molina et Alejandro We also address the issues of aggregation of gray level images before and after edge detection and of filtering.

Edge Detection

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