CORDIS
EU research results

CORDIS

English EN

Beyond Features: Similarity-Based Pattern Analysis and Recognition

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.
Leaflet | Map data © OpenStreetMap contributors, Credit: EC-GISCO, © EuroGeographics for the administrative boundaries

Coordinator

UNIVERSITA CA' FOSCARI VENEZIA

Address

Dorsoduro 3246
30123 Venezia

Italy

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 319 940

Administrative Contact

Marcello Pelillo (Prof.)

Participants (5)

Sort alphabetically

Sort by EU Contribution

Expand all

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH

Switzerland

EU Contribution

€ 267 600

UNIVERSITA DEGLI STUDI DI VERONA

Italy

EU Contribution

€ 276 600

TECHNISCHE UNIVERSITEIT DELFT

Netherlands

EU Contribution

€ 275 400

INSTITUTO SUPERIOR TECNICO

Portugal

EU Contribution

€ 279 000

UNIVERSITY OF YORK

United Kingdom

EU Contribution

€ 229 440

Project information

Grant agreement ID: 213250

Status

Closed project

  • Start date

    1 April 2008

  • End date

    30 September 2011

Funded under:

FP7-ICT

  • Overall budget:

    € 2 171 104

  • EU contribution

    € 1 647 980

Coordinated by:

UNIVERSITA CA' FOSCARI VENEZIA

Italy