Abstract The “Big Data” era has arisen, driven by the increasing availability of data from
multiple sources such as social media, online transactions, network sensors or mobile …

The use of approximate graph matching for frequent subgraph mining has been identified in
different applications as a need. To meet this need, several algorithms have been …

Graph-based data representations are an important research topic due to the suitability of
this kind of data structure to model entities and the complex relations among them. In …

Spatial relations among objects and object parts play a fundamental role in the human
perception and understanding of images, thus becoming very relevant in the computational …

Abstract The Bag-of-Visual-Words model has emerged as an effective approach to represent
local video features for human actions classification. However, one of the major challenges …

Frequent approximate subgraph (FAS) mining is used in applications where it is important to
take into account some tolerance under slight distortions in the data. Following this …

In recent years, an increasing number of works have been reported that modeling data as
multi-graphs more efficiently solves some practical problems. In these contexts, data mining …

La clasificación de acciones humanas en video es un área del conocimiento muy activa en
la comunidad científica de la visión por computador. El objetivo de este campo de …

Human action classification is an important task in computer vision. The Bag-of-Words
model is a representation method very used in action classification techniques. In this work …

Esta investigación teórica aborda el problema del reconocimiento automá tico de objetos
en imá genes digitales bidimensionales (2D). Esta temá tica se enmarca dentro del campo …