Vector quantizing feature space with a regular lattice - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2007

Vector quantizing feature space with a regular lattice

Résumé

Most recent class-level object recognition systems work with visual words, i.e., vector quantized local descriptors. In this paper we examine the feasibility of a data- independent approach to construct such a visual vocabulary, where the feature space is discretized using a regular lattice. Using hashing techniques, only non-empty bins are stored, and fine-grained grids become possible in spite of the high dimensionality of typical feature spaces. Based on this representation, we can explore the structure of the feature space, and obtain state-of-the-art pixelwise classification results. In the case of image classification, we introduce a class-specific feature selection step, which takes the spatial structure of SIFT-like descriptors into account. Results are reported on the Graz02 dataset.
Fichier principal
Vignette du fichier
Tuytelaars_Schmid-lattice-iccv07.pdf (608.37 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

inria-00548675 , version 1 (20-12-2010)

Identifiants

Citer

Tinne Tuytelaars, Cordelia Schmid. Vector quantizing feature space with a regular lattice. ICCV - 11th IEEE International Conference on Computer Vision, Oct 2007, Rio de Janeiro, Brazil. pp.1-8, ⟨10.1109/ICCV.2007.4408924⟩. ⟨inria-00548675⟩
179 Consultations
369 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More