Data Set Used
A recent paper posed the question: " Graph Matching: What are we really talking about? ". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching… (More)
This paper presents a novel method to count people for video surveillance applications. Methods in the literature either follow a direct approach, by first detecting people and then counting them, or an indirect approach, by establishing a relation between some easily detectable scene features and the estimated number of people. The indirect approach is… (More)
Graphs are an extremely general and powerful data structure. In pattern recognition and computer vision, graphs are used to represent patterns to be recognized or classified. Detection of maximum common sub-graph (MCS) is useful for matching, comparing and evaluate the similarity of patterns. MCS is a well known NP-complete problem for which optimal and… (More)
In this paper we will try to characterize the role that graphs are conquering within the Pattern Recognition field. To this aim, a taxonomy built considering the most common applications of graph based techniques in the Pattern Recognition and Image Processing field is presented and discussed.
In a video surveillance system the object tracking is one of the most challenging problem. In fact objects in the world exhibit complex interactions. When captured in a video sequence, some interactions manifest themselves as occlusions. A visual tracking system must be able to track objects which are partially or even fully occluded. In this paper we… (More)
A graph g is called a maximum common subgraph of two graphs, g 1 and g 2 , if there exists no other common subgraph of g 1 and g 2 that has more nodes than g. For the maximum common subgraph problem, exact and inexact algorithms are known from the literature. Nevertheless, until now no effort has been done for characterizing their performance, mainly for… (More)
In this paper we present a quantitative comparison between two approaches, Graph Kernels and Symbolic Learning, within a classification scheme. The experimental case-study is the predictive toxicology evaluation, that is the inference of the toxic characteristics of chemical compounds from their structure. The results demonstrate that both approaches are… (More)