SIMBAD

Beyond Features: Similarity-Based Pattern Analysis and Recognition

 Coordinatore UNIVERSITA CA' FOSCARI VENEZIA 

 Organization address address: Via Torino 155
city: Venezia Mestre
postcode: 30172

contact info
Titolo: Prof.
Nome: Marcello
Cognome: Pelillo
Email: send email
Telefono: +39 041 2348440
Fax: +39 041 2348419

 Nazionalità Coordinatore Italy [IT]
 Totale costo 2˙171˙104 €
 EC contributo 1˙647˙980 €
 Programma FP7-ICT
Specific Programme "Cooperation": Information and communication technologies
 Code Call FP7-ICT-2007-C
 Funding Scheme CP
 Anno di inizio 2008
 Periodo (anno-mese-giorno) 2008-04-01   -   2011-09-30

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    UNIVERSITA CA' FOSCARI VENEZIA

 Organization address address: Via Torino 155
city: Venezia Mestre
postcode: 30172

contact info
Titolo: Prof.
Nome: Marcello
Cognome: Pelillo
Email: send email
Telefono: +39 041 2348440
Fax: +39 041 2348419

IT (Venezia Mestre) coordinator 0.00
2    EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH

 Organization address address: Raemistrasse
city: ZUERICH
postcode: 8092

contact info
Titolo: Prof.
Nome: Joachim
Cognome: Buhmann
Email: send email
Telefono: 41446326496
Fax: 41446321562

CH (ZUERICH) participant 0.00
3    INSTITUTO SUPERIOR TECNICO

 Organization address address: Avenida Rovisco Pais
city: LISBOA
postcode: 1049-001

contact info
Titolo: Dr.
Nome: Teresa
Cognome: Malhoa
Email: send email
Telefono: +351 218417731
Fax: +351 218478619

PT (LISBOA) participant 0.00
4    TECHNISCHE UNIVERSITEIT DELFT

 Organization address address: Stevinweg
city: DELFT
postcode: 2628 CN

contact info
Titolo: Mr.
Nome: Dennis F.
Cognome: Van Doorn
Email: send email
Telefono: 31152789344
Fax: 31152787022

NL (DELFT) participant 0.00
5    UNIVERSITA DEGLI STUDI DI VERONA

 Organization address address: VIA DELL' ARTIGLIERE 8
city: VERONA
postcode: 37129

contact info
Titolo: Dr.
Nome: Giacomina
Cognome: Bruttomesso
Email: send email
Telefono: +39 0458027071
Fax: +39 0458027068

IT (VERONA) participant 0.00
6    UNIVERSITY OF YORK

 Organization address address: HESLINGTON HALL
city: YORK
postcode: YO10 5DD

contact info
Titolo: Mr.
Nome: Chris
Cognome: Barber
Email: send email
Telefono: 441904000000
Fax: 441904000000

UK (YORK) participant 0.00

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

objects    world    real    dis    obtain    pattern    similarity    recognition    problem    classified    learning    theoretical    purely    computational   

 Obiettivo del progetto (Objective)

Traditional pattern recognition techniques are centered around the notion of 'feature'. According to this view, the objects to be classified are represented in terms of properties that are intrinsic to the object itself. Hence, a typical pattern recognition system makes its decisions by simply looking at one or more feature vectors provided as input. The strength of this approach is that it can leverage a wide range of mathematical tools ranging from statistics, to geometry, to optimization. However, in many real-world applications a feasible feature-based description of objects might be difficult to obtain or inefficient for learning purposes. In these cases, it is often possible to obtain a measure of the (dis)similarity of the objects to be classified, and in some applications the use of dissimilarities (rather than features) makes the problem more viable. In the last few years, researchers in pattern recognition and machine learning are becoming increasingly aware of the importance of similarity information per se. Indeed, by abandoning the realm of vectorial representations one is confronted with the challenging problem of dealing with (dis)similarities that do not necessarily obey the requirements of a metric. This undermines the very foundations of traditional pattern recognition theories and algorithms, and poses totally new theoretical and computational questions. In this project we aim at undertaking a thorough study of several aspects of purely similarity-based pattern analysis and recognition methods, from the theoretical, computational, and applicative perspective. We aim at covering a wide range of problems and perspectives. We shall consider both supervised and unsupervised learning paradigms, generative and discriminative models, and our interest will range from purely theoretical problems to real-world practical applications.

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