Object Classification Based on Graph Kernels

Amal Mahboubi (1) , Luc Brun (1) , François-Xavier Dupé (1)

1 Equipe Image - Laboratoire GREYC - UMR6072

Automatic object recognition plays a central role in numerous applications, such as image retrieval and robot navigation. A now classical strategy consists to compute a bag of features within a sliding window and to compare this bag with precomputed models. One main drawback of this approach is the use of an unstructured bag of features which do not allow to take into account relationships which may be defined on structured objects. Graphs are natural data structures to model such relationships with nodes representing features and edges encoding relationships between them. However, usual distances between graphs such as the graph edit distance do not satisfy all the properties of a metric and classifiers defined on these distances are mainly restricted to the K nearest neighbors method. This article describes an image object classification method based on a definite positive graph kernel inducing a metric between graphs. This kernel may thus be combined with numerous classification algorithms.

Mots clés: Object classification; Graph kernels

Domaines: Traitement des images [eess.IV]

Amal Mahboubi, Luc Brun, François-Xavier Dupé. Object Classification Based on Graph Kernels. HPCS 2010, Jun 2010, Caen, France. pp.385-389. ⟨hal-00954566⟩