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Multimodal Signal Processing and Machine Learning on Graphs

Total activity: 24
Type of activity
Competitive project
Acronym
MALEGRA
Funding entity
MIN DE ECONOMIA Y COMPETITIVIDAD
Funding entity code
TEC2016-75976-R
Amount
301.290,00 €
Start date
2016-12-30
End date
2020-12-29
Keywords
Digital Society; Innovation; Health, Wellness & Inclusion; grafos, aprendizaje automático, aprendizaje profundo, big data, datos heterogéneos, datos multimodales, deep learning, fusion, fusión, graph signal processing, graphs, heterogeneous data, machine learning, multimodal data, procesado de señal en grafos, registration, registro
Abstract
The goal of this project is to study and develop tools combining graph signal representation and processing ideas with machine learning
technology. These tools will be used in the context of applications where the size and/or the heterogeneity of the data represent challenges
of the Big Data era. The development of technologies related to the capture, storage, search, distribution, transfer, analysis and visualization of ever growing heterogeneous datasets entails tremendous difficulties. At the same time,
these difficulties open new opportunities and this development has become a major trend in the field of Information and Communication
Technology. The research performed in this project targets applications such as multi-view representations, video analysis, remote sensing
for earth monitoring, person identification, health monitoring, medical imaging, genomics, etc.
The project has 4 major objectives. The first two objectives concentrate most of the development of theoretical and basic tools within the
project. Within them, we will investigate the creation, analysis, segmentation, filtering and merging of graph structures of heterogeneous
multimodal data and on the combination of these ideas with machine learning techniques. This combination with machine learning will be
used for several different purposes. In particular, to provide a classification decision, to learn a mapping or a model to be used in a data
processing architecture, to learn features that outperform handcrafted equivalents or to aggregate several features to create a signal to be
further processed.
The last two objectives of the project focus on the application of the techniques and tools developed in the first two objectives in complex
challenges that deal with big and heterogeneous data. In particular, these techniques and tools will be used to study the identification of
persons in broadcast TV programs, the optimal encoding of depth maps in multi-view plus depth representations, the radiometric
estimation and object detection in SAR and PolSAR images, the classification of multispectral and hyperspectral images, the
understanding of brain changes during the evolution of Alzheimers disease, the inference of gene regulatory networks and the
segmentation, tracking, indexing and super-resolution of multimodal video sequences.
Scope
Adm. Estat
Plan
Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016
Call year
2016
Funcding program
Programa Estatal de I+D+i Orientada a los Retos de la Sociedad
Funding call
Retos de Investigación: Proyectos de I+D+i
Grant institution
Gobierno De España. Ministerio De Economía Y Competitividad, Mineco

Participants

Scientific and technological production

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