ANTHOINE , Sandrine

In this paper, the inversion of a linear operator is tackled by a procedure called iterative shrinkage. Iterative shrinkage is a procedure that minimizes a functional balancing quadratic discrepancy terms with Lp regularization terms. In this work, we propose to replace the classical quadratic discrepancy terms with adaptive ones. These...

n this paper, a general framework for the inversion of a linear operator in the case where one seeks several components from several observations is presented. The estimation is done by minimizing a functional balancing discrepancy terms by regularization terms. The regularization terms are adapted norms that enforce the desired properties of...

Nous proposons dans ce papier d'étudier les métriques de similarité entre images dans le cadre des problèmes inverses tels que la déconvolution et la séparation de sources. La métrique de similarité que nous proposons est basée sur les notions de théorie de l'information (distance de Kullback-Leibler) et est combinée avec une transformée en...

In this paper, we define a similarity measure to compare images in the context of (indexing and) retrieval. We use the Kullback-Leibler (KL) divergence to compare sparse multiscale image descriptions in a wavelet domain. The KL divergence between wavelet coefficient distributions has already been used as a similarity measure between images. The...

In this paper we address the problem of scalable video indexing. We propose a new framework combining sparse spatial multiscale patches and Group of Pictures (GoP) motion patches. The distributions of these sets of patches are compared via the Kullback-Leibler divergence estimated in a non-parametric framework using a k-th Nearest Neighbor...

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,...

Submitted: April 07, 2009

In this paper we address the task of image categorization using a new similarity measure on the space of Sparse Multiscale Patches (SMP). SMPs are based on a multiscale transform of the image and provide a global representation of its content. At each scale, the probability density function (pdf ) of the SMPs is used as a description of the...

This paper tackles the problem of scalable video indexing. We propose a new framework combining spatial and motion patch descriptors. The spatial descriptors are based on a multiscale description of the image and are called Sparse Multiscale Patches. We propose motion patch descriptors based on block motion that describe the motion in a Group...

In this paper, we define a similarity measure between images in the context of (indexing and) retrieval. We use the Kullback-Leibler (KL) divergence to compare sparse multiscale image representations. The KL divergence between parameterized marginal distributions of wavelet coefficients has already been used as a similarity measure between...

HD video content represents a tremendous quantity of information that cannot be easily handled by all types of devices. Hence the scalability issues in its processing have become a focus of interest in HD video coding technologies. In this chapter, we focus on the natural scalability of hierarchical transforms to tackle video indexing and...