Showing papers by "Francesc Serratosa published in 2009"

Graph-Based k-Means Clustering: A Comparison of the Set Median versus the Generalized Median Graph

Abstract: In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph.

On the Computation of the Common Labelling of a Set of Attributed Graphs

Abstract: In some methodologies, it is needed a consistent common labelling between the vertices of a set of graphs, for instance, to compute a representative of a set of graphs. This is a NP-problem with an exponential computational cost depending on the number of nodes and the number of graphs. The aim of this paper is twofold. On one hand, we aim to establish a technical methodology to define this problem for the present and further research. On the other hand, we present two sub-optimal algorithms to compute the labelling between a set of graphs. Results show that our new algorithms are able to find a consistent common labelling while reducing, most of the times, the mean distance of the AG set.

A Structural and Semantic Probabilistic Model for Matching and Representing a Set of Graphs

Abstract: This article presents a structural and probabilistic framework for representing a class of attributed graphs with only one structure. The aim of this article is to define a new model, called Structurally-Defined Random Graphs. This structure keeps together statistical and structural information to increase the capacity of the model to discern between attributed graphs within or outside the class. Moreover, we define the match probability of an attributed graph respect to our model that can be used as a dissimilarity measure. Our model has the advantage that does not incorporate application dependent parameters such as edition costs. The experimental validation on a TC-15 database shows that our model obtains higher recognition results, when there is moderate variability of the class elements, than several structural matching algorithms. Indeed in our model fewer comparisons are needed.

Tracking deformable objects and dealing with same class object occlusion

Abstract: Presentado al International Conference on Computer Vision Theory and Applications (VISAPP/2009) celebrado en Lisboa (Portugal).

Experimental Assessment of Probabilistic Integrated Object Recognition and Tracking Methods

Abstract: This paper presents a comparison of two classifiers that are used as a first step within a probabilistic object recognition and tracking framework called PIORT. This first step is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. One of the implemented classifiers is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results show that, on one hand, both classifiers (although they are very different approaches) yield a similar performance when they are integrated within the tracking framework. And on the other hand, our object recognition and tracking framework obtains good results when compared to other published tracking methods in video sequences taken with a moving camera and including total and partial occlusions of the tracked object.

Median Graph Computation by Means of a Genetic Approach Based on Minimum Common Supergraph and Maximum Common Subgraph

Abstract: Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present a new genetic algorithm for the median graph computation. A set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity, show that we obtain good approximations of the median graph. Finally, we use the median graph in a real nearest neighbour classification showing that it leaves the box of the only-theoretical concepts and demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.