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Displaying 1-20 out of 20 total
Guest Editors' Introduction to the Special Section on Energy Minimization Methods in Computer Vision and Pattern Recognition
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Mário A.T. Figueiredo, Edwin R. Hancock, Marcello Pelillo, Josiane Zerubia
Issue Date:November 2003
pp. 1361-1363
No summary available.
 
Structured Labels in Random Forests for Semantic Labelling and Object Detection
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Peter Kontschieder,Samuel Rota Bulo,Marcello Pelillo,Horst Bischof
Issue Date:October 2014
pp. 1-1
Ensembles of randomized decision trees, known as Random Forests, have become a valuable machine learning tool for addressing many computer vision problems. Despite their popularity, few works have tried to exploit contextual and structural information in r...
 
A Game-Theoretic Approach to Hypergraph Clustering
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Samuel Rota Bulò,Marcello Pelillo
Issue Date:June 2013
pp. 1312-1327
Hypergraph clustering refers to the process of extracting maximally coherent groups from a set of objects using high-order (rather than pairwise) similarities. Traditional approaches to this problem are based on the idea of partitioning the input data into...
 
Structured class-labels in random forests for semantic image labelling
Found in: Computer Vision, IEEE International Conference on
By Peter Kontschieder,Samuel Rota Bulo,Horst Bischof,Marcello Pelillo
Issue Date:November 2011
pp. 2190-2197
In this paper we propose a simple and effective way to integrate structural information in random forests for semantic image labelling. By structural information we refer to the inherently available, topological distribution of object classes in a given im...
 
Probabilistic Clustering Using the Baum-Eagon Inequality
Found in: Pattern Recognition, International Conference on
By Samuel Rota Bulò, Marcello Pelillo
Issue Date:August 2010
pp. 1429-1432
The paper introduces a framework for clustering data objects in a similarity-based context. The aim is to cluster objects into a given number of classes without imposing a hard partition, but allowing for a soft assignment of objects to clusters. Our appro...
 
Integrating Boundary Information in Pairwise Segmentation
Found in: Image Analysis and Processing, International Conference on
By Andrea Torsello, Marco Di Gesu, Marcello Pelillo
Issue Date:September 2007
pp. 23-28
Proximity-based, or pairwise, data clustering techniques are gaining increasing popularity due to their versatility and their ability to easily integrate information of different nature. Despite this, most applications to image segmentation incorporate onl...
 
Matching Relational Structures using the Edge-Association Graph
Found in: Image Analysis and Processing, International Conference on
By Andrea Torsello, Andrea Albarelli, Marcello Pelillo
Issue Date:September 2007
pp. 775-780
The matching of relational structures is a problem that pervades computer vision and pattern recognition research. A classic approach is to reduce the matching problem into one of search of a maximum clique in an auxiliary structure: the association graph....
 
Dominant Sets and Pairwise Clustering
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Massimiliano Pavan, Marcello Pelillo
Issue Date:January 2007
pp. 167-172
We develop a new graph-theoretic approach for pairwise data clustering which is motivated by the analogies between the intuitive concept of a cluster and that of a dominant set of vertices, a notion introduced here which generalizes that of a maximal compl...
 
Polynomial-Time Metrics for Attributed Trees
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Andrea Torsello, Džena Hidovic-Rowe, Marcello Pelillo
Issue Date:July 2005
pp. 1087-1099
We address the problem of comparing attributed trees and propose four novel distance measures centered around the notion of a maximal similarity common subtree. The proposed measures are general and defined on trees endowed with either symbolic or continuo...
 
Four Metrics for Efficiently Comparing Attributed Trees
Found in: Pattern Recognition, International Conference on
By Andrea Torsello, Dzena Hidovic, Marcello Pelillo
Issue Date:August 2004
pp. 467-470
We address the problem of comparing attributed trees and propose four novel distance metrics centered around the notion of a maximal similarity common subtree, and hence can be computed in polynomial time. We experimentally validate the usefulness of our m...
 
Relaxation Labeling Processes for Protein Secondary Structure Prediction
Found in: Pattern Recognition, International Conference on
By Giacomo Colle, Marcello Pelillo
Issue Date:August 2004
pp. 355-358
The prediction of protein secondary structure is a classical problem in bioinformatics, and in the past few years several machine learning techniques have been proposed to attack it. From an abstract pattern recognition viewpoint, the problem can be formul...
 
Unsupervised Texture Segmentation by Dominant Sets and Game Dynamics
Found in: Image Analysis and Processing, International Conference on
By Massimiliano Pavan, Marcello Pelillo
Issue Date:September 2003
pp. 302
We develop a framework for the unsupervised texture segmentation problem based on dominant sets, a new graph-theoretic concept that has proven to be relevant in pairwise data clustering as well as image segmentation problems. A remarkable correspondence be...
 
Hierarchical Matching of Panoramic Images
Found in: Image Analysis and Processing, International Conference on
By Roland Glantz, Marcello Pelillo, Walter G. Kropatsch
Issue Date:September 2003
pp. 328
When matching regions from
 
A New Graph-Theoretic Approach to Clustering and Segmentation
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Massimiliano Pavan, Marcello Pelillo
Issue Date:June 2003
pp. 145
We develop a framework for the image segmentation problem based on a new graph-theoretic formulation of clustering. The approach is motivated by the analogies between the intuitive concept of a cluster and that of a dominant set of vertices, a novel notion...
 
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Marcello Pelillo
Issue Date:November 2002
pp. 1535-1541
<p><b>Abstract</b>—Motivated by our recent work on rooted tree matching, in this paper we provide a solution to the problem of matching two free (i.e., unrooted) trees by constructing an association graph whose maximal cliques are in one-...
 
Attributed Tree Homomorphism Using Association Graphs
Found in: Pattern Recognition, International Conference on
By Massimo Bartoli, Marcello Pelillo, Kaleem Siddiqi, Steven W. Zucker
Issue Date:September 2000
pp. 2133
The matching of hierarchical relational structures is of significant interest in computer vision and pattern recognition. We have recently introduced a new solution to this problem, based on a maximum clique formulation in an (derived) “association graph.”...
 
Matching Hierarchical Structures Using Association Graphs
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Marcello Pelillo, Kaleem Siddiqi, Steven W. Zucker
Issue Date:November 1999
pp. 1105-1120
<p><b>Abstract</b>—It is well-known that the problem of matching two relational structures can be posed as an equivalent problem of finding a maximal clique in a (derived) “association graph.” However, it is not clear how to apply this ap...
 
Attributed Tree Matching and Maximum Weight Cliques
Found in: Image Analysis and Processing, International Conference on
By Marcello Pelillo, Kaleem Siddiqi, Steven W. Zucker
Issue Date:September 1999
pp. 1154
A classical way of matching relational structures consists of finding a maximum clique in a derived
 
A Unifying Framework for Relational Structure Matching
Found in: Pattern Recognition, International Conference on
By Marcello Pelillo
Issue Date:August 1998
pp. 1316
No summary available.
 
Is data clustering in adversarial settings secure?
Found in: Proceedings of the 2013 ACM workshop on Artificial intelligence and security (AISec '13)
By Ignazio Pillai, Marcello Pelillo, Battista Biggio, Davide Ariu, Fabio Roli, Samuel Rota Bulò
Issue Date:November 2013
pp. 87-98
Clustering algorithms have been increasingly adopted in security applications to spot dangerous or illicit activities. However, they have not been originally devised to deal with deliberate attack attempts that may aim to subvert the clustering process its...
     
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