Sparse Multiscale Pacthes for Image Processing - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2009

Sparse Multiscale Pacthes for Image Processing

Résumé

This paper presents a framework to define an objective measure of the similarity (or dissimilarity) between two images for image processing. The problem is twofold: 1) define a set of features that capture the information contained in the image relevant for the given task and 2) define a similarity measure in this feature space. In this paper, we propose a feature space as well as a statistical measure on this space. Our feature space is based on a global descriptor of the image in a multiscale transformed domain. After decomposition into a Laplacian pyramid, the coefficients are arranged in intrascale/interscale/interchannel patches which reflect the dependencies of neighboring coefficients in presence of specific structures or textures. At each scale, the probability density function (pdf) of these patches is used as a descriptor of the relevant information. Because of the sparsity of the multiscale transform, the most significant patches, called Sparse Multiscale Patches (SMP), describe efficiently these pdfs. We propose a statistical measure (the Kullback-Leibler divergence) based on the comparison of these probability density functions. Interestingly, this measure is estimated via the nonparametric, k-th nearest neighbor framework without explicitly building the pdfs. This framework is applied to a query-by-example image retrieval method. Experiments on two publicly available databases showed the potential of our SMP approach for this task. In particular, it performed comparably to a SIFT-based retrieval method and two versions of a fuzzy segmentation-based method (the UFM and CLUE methods), and it exhibited some robustness to different geometric and radiometric deformations of the images.
Fichier principal
Vignette du fichier
ETVCPiro.pdf (1.18 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00382776 , version 1 (11-05-2009)

Identifiants

Citer

Paolo Piro, Sandrine Anthoine, Eric Debreuve, Michel Barlaud. Sparse Multiscale Pacthes for Image Processing. Emerging Trends in Visual Computing (ETVC), Nov 2008, Palaiseau, France. pp.284 - 304, ⟨10.1007/978-3-642-00826-9_13⟩. ⟨hal-00382776⟩
139 Consultations
126 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More