SIMBAD
Beyond Features: Similarity-Based Pattern Analysis and Recognition
| Coordinatore | UNIVERSITA CA' FOSCARI VENEZIA
Organization address
address: Via Torino 155 contact info |
| 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
| # | ||||
|---|---|---|---|---|
| 1 |
UNIVERSITA CA' FOSCARI VENEZIA
Organization address
address: Via Torino 155 contact info |
IT (Venezia Mestre) | coordinator | 0.00 |
| 2 |
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH
Organization address
address: Raemistrasse contact info |
CH (ZUERICH) | participant | 0.00 |
| 3 |
INSTITUTO SUPERIOR TECNICO
Organization address
address: Avenida Rovisco Pais contact info |
PT (LISBOA) | participant | 0.00 |
| 4 |
TECHNISCHE UNIVERSITEIT DELFT
Organization address
address: Stevinweg contact info |
NL (DELFT) | participant | 0.00 |
| 5 |
UNIVERSITA DEGLI STUDI DI VERONA
Organization address
address: VIA DELL' ARTIGLIERE 8 contact info |
IT (VERONA) | participant | 0.00 |
| 6 |
UNIVERSITY OF YORK
Organization address
address: HESLINGTON HALL contact info |
UK (YORK) | participant | 0.00 |
Mappa
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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.