SIMBAD - Similarity-Based Pattern Analysis and Recognition

Profile Card Research project

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Starting date
April 1, 2008

Duration (months)
36

Departments
Computer Science, Public Health and Community Medicine

Person in charge
Murino Vittorio

URL
http://simbad-fp7.eu/

Description

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.

Sponsors:

Unione Europea
Funds: assigned and managed by the department
Syllabus: EUROPA - Progetti Europei

Project participants

Marcella Bellani
Level 1 Managing Doctor
Manuele Bicego
Assistant Professor
Anna Caterina Carli
Umberto Castellani
Assistant Professor
Stefania Cerruti
Marco Cristani
Assistant Professor
Vittorio Murino
Full Professor
Alessandro Perina
Mehmet Aydin Ulas
ornamento
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