Multi-Layer Local Graph Words for Object Recognition - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

Multi-Layer Local Graph Words for Object Recognition

Résumé

In this paper, we propose a new multi-layer structural approach for the task of object based image retrieval. In our work we tackle the problem of structural organization of local features. The structural features we propose are nested multi-layered local graphs built upon sets of SURF feature points with Delaunay triangulation. A Bag-of-Visual-Words (BoVW) framework is applied on these graphs, giving birth to a Bag-of-Graph-Words representation. The multi-layer nature of the descriptors consists in scaling from trivial Delaunay graphs - isolated feature points - by increasing the number of nodes layer by layer up to graphs with maximal number of nodes. For each layer of graphs its own visual dictionary is built. The experiments conducted on the SIVAL and Caltech-101 data sets reveal that the graph features at different layers exhibit complementary performances on the same content and perform better than baseline BoVW approach. The combination of all existing layers, yields significant improvement of the object recognition performance compared to single level approaches.
Fichier principal
Vignette du fichier
CameraReadyMMM2012_12p.pdf (1.27 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-00637120 , version 1 (30-10-2011)

Identifiants

Citer

Svebor Karaman, Jenny Benois-Pineau, Rémi Mégret, Aurélie Bugeau. Multi-Layer Local Graph Words for Object Recognition. International Conference on MultiMedia Modeling, Jan 2012, Klagenfurt, Austria. pp.29-39. ⟨hal-00637120⟩
193 Consultations
168 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More