Paolo Piro

Currently I am PostDoc researcher at the Italian Institute of Technology (IIT), in the Computer Imaging facility.

My PostDoc research is in computer vision and machine learning, and is mainly focused on the following topics:

  • cluster analysis for computer-aided drug design
  • biomedical image analysis and classification
Recently I have defended my Ph.D. thesis at the University of Nice-Sophia Antipolis, I3S Laboratory, under the supervision of Prof. Michel Barlaud.
My Ph.D. thesis was also in co-tutorship with Prof. Giulio Iannello of the Campus Bio-Medico University of Rome.
I have collaborated with Frank Nielsen (LIX, Ecole Polytechnique), Richard Nock (CEREGMIA, University of Antilles-Guyane), Eric Debreuve (I3S/CNRS, University of Nice-Sophia Antipolis) and Sandrine Anthoine (LATP, University of Provence/CNRS).

My Ph.D. thesis is mainly focused on the following topics:
  • content-based image indexing
  • data structures for fast nearest neighbor queries
  • prototype-based supervised learning
  • automatic categorization of real-world and medical images

Ph.D. thesis

Paolo Piro. "Learning prototype-based classification rules in a boosting framework: application to real-world and medical image categorization".
University of Nice-Sophia Antipolis, January 2011.

Boosting k-NN

We have generalized the classic k-NN classification in a supervised learning framework.
Namely, we have proposed an instance-based classification rule, which can be induced by our simple UNN algorithm minimizing a convex risk function on training data.
Our learning algorithm is inspired by AdaBoost in that it linearly combines predictions from learned prototypes.
UNN enables consistent data reduction and is adapted to multi-class classification.

Medical image retrieval

We have addressed the problem of annotating medical images for computer-aided diagnosis.
In particular, we have proposed a novel multi-class leaning algorithm, MLNN, for leveraging prototype-based classification.
We have tested MLNN for automatic body part recognition on a set of medical radiographs taken from the clinical routine. Automatic indexing of such images is expected to be the first step towards a full computer-aided diagnosis system.

ICOS-HD project

My PhD thesis has been part of the ANR research project Icos-HD.
The main goal of Icos-HD was to define a suitable framework for scalable content-based description of HD images and videos.
In particular, we concentrated on low-level visual image descriptors, and proposed a new descriptor, called SMP.
SMP exploits space and scale correlations of Laplacian pyramid coefficients for representing visual information in a sparse way.

Bregman nearest neighbors

Nearest neighbor (NN) search is a crucial tool in content-based image indexing and retrieval.
We have addressed the problem of searching NN efficiently when classic distance metrics are replaced by Bregman divergences. Namely, we have considered two data structures that have proven very effective in applications. First, we have improved Bregman Ball trees, also adapting them to symmetrized divergences.
Then, we have generalized vantage point trees to Bregman divergences.