Synthesis of Function-Described Graphs and Clustering of Attributed Graphs

Self Cite

Abstract. In this paper we present a summary of some of the research that we are developing in the Institute of Robotics of the CSIC-UPC, in the field of Learning and Robot Vision for autonomous mobile robots. We describe the problems that we have found and some solutions that have been applied in two issues: tracking objects and learning and recognition of 3D objects in robotic environments. We will explain some of the results accomplished.

Section: Learning and Recognising 3d Objects Represented By Multiple mentioning

confidence: 99%

Robot Vision for Autonomous Object Learning and Tracking

Sanfeliu

2003

Self Cite

“…Among them we could name: [4] where optimal pairwise labelings are required or [5] and [6] where, in this case, the CL computation is based on sub-optimal pairwise labelings. Although [5] is quite more effective than [6], both share the same weakness: the use of pairwise labelings, where a simple labeling error taken at initial stages could derive in a bad global result. Moreover [5] have tendency to add extra nodes in the final CL, which might be not desired in some applications.…”

Section: Introductionmentioning

confidence: 99%

Graduated Assignment Algorithm for Finding the Common Labelling of a Set of Graphs

Solé-Ribalta

Serratosa

2010

Self Cite

Abstract. In pattern recognition applications, it is useful to represent objects by attributed graphs considering their structural properties. Besides, some graph matching problems need a Common Labelling between vertices of a set of graphs. Computing this Common Labelling is an NP-complete problem. Stateof-the-art algorithms are composed by two steps: in the first, they compute all pairwise labellings among the graphs and in the second, they combine this information to obtain a Common Labelling. The drawback of these methods is that global information is only considered in the second step. To solve this problem, by reducing the Common Labelling problem to the quadratic assignment one, all graphs nodes are labelled to a virtual structure whereby the Common Labeling is generated using global information. We tested the algorithm on both real-world and synthetic data. We show that the algorithm offers better performance than a reference method with same computational cost.

“…Random Graphs such as FirstOrder Random Graphs (FORGs) [20], Function-Described Graphs (FDGs) [17,18] and Second-Order Random Graphs (SORGs) [16]; a Maximally General Prototype [4]; the Median Graph [9] and the Barycenter Graph [8] have been proposed as representatives of a set of graphs, among others. Most of these methods suffer from a prohibitive computation time or are limited to a restricted family of graphs.…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Two Representatives of a Set of Graphs: Median vs. Barycenter Graph

Bardají

Ferrer

Sanfeliu

2010

Self Cite

Abstract. In this paper we consider two existing methods to generate a representative of a given set of graphs, that satisfy the following two conditions. On the one hand, that they are applicable to graphs with any kind of labels in nodes and edges and on the other hand, that they can handle relatively large amount of data. Namely, the approximated algorithms to compute the Median Graph via graph embedding and a new method to compute the Barycenter Graph. Our contribution is to give a new algorithm for the barycenter computation and to compare it to the median Graph. To compare these two representatives, we take into account algorithmic considerations and experimental results on the quality of the representative and its robustness, on several datasets.