19,052 research outputs found
Structuration de bases multimédia pour une exploration visuelle
The large increase in multimedia data volume requires the development of effective solutions for visual exploration of multimedia databases. After reviewing the visualization process involved, we emphasis the need of data structuration. The main objective of this thesis is to propose and study clustering and classification of multimedia database for their visual exploration.We begin with a state of the art detailing the data and the metrics we can produce according to the nature of the variables describing each document. Follows a review of the projection and classification techniques. We also present in detail the Spectral Clustering method.Our first contribution is an original method that produces fusion of metrics using rank correlations. We validate this method on an animation movie database coming from an international festival. Then we propose a supervised classification method based on rank correlation. This contribution is evaluated on a multimedia challenge dataset. Then we focus on Spectral Clustering methods. We test a supervised Spectral Clustering technique and compare to state of the art methods. Finally we examine active semi-supervised Spectral Clustering methods. In this context, we propose and validate constraint propagation techniques and strategies to improve the convergence of these active methods.La forte augmentation du volume de données multimédia impose la mise au point de solutions adaptées pour une exploration visuelle efficace des bases multimédia. Après avoir examiné les processus de visualisation mis en jeu, nous remarquons que ceci demande une structuration des données. L’objectif principal de cette thèse est de proposer et d’étudier ces méthodes de structuration des bases multimédia en vue de leur exploration visuelle.Nous commençons par un état de l’art détaillant les données et les mesures que nous pouvons produire en fonction de la nature des variables décrivant les données. Suit un examen des techniques de structuration par projection et classification. Nous présentons aussi en détail la technique du Clustering Spectral sur laquelle nous nous focaliserons ensuite.Notre première réalisation est une méthode originale de production et fusion de métriques par corrélation de rang. Nous testons cette première méthode sur une base multimédia issue de la vidéothèque d’un festival de films. Nous continuons ensuite par la mise au point d’une méthode de classification supervisée par corrélation que nous testons avec les données vidéos d’un challenge de la communauté multimédia. Ensuite nous nous focalisons sur les techniques du Clustering Spectral. Nous testons une technique de Clustering Spectral supervisée que nous comparons aux techniques de l’état de l’art. Et pour finir nous examinons des techniques du Clustering Spectral semi-supervisé actif. Dans ce contexte, nous proposons et validons des techniques de propagation d’annotations et des stratégies permettant d’améliorer la convergence de ces méthodes de classement
Inhomogeneous graph trend filtering via a l2,0 cardinality penalty
We study estimation of piecewise smooth signals over a graph. We propose a
-norm penalized Graph Trend Filtering (GTF) model to estimate
piecewise smooth graph signals that exhibits inhomogeneous levels of smoothness
across the nodes. We prove that the proposed GTF model is simultaneously a
k-means clustering on the signal over the nodes and a minimum graph cut on the
edges of the graph, where the clustering and the cut share the same assignment
matrix. We propose two methods to solve the proposed GTF model: a spectral
decomposition method and a method based on simulated annealing. In the
experiment on synthetic and real-world datasets, we show that the proposed GTF
model has a better performances compared with existing approaches on the tasks
of denoising, support recovery and semi-supervised classification. We also show
that the proposed GTF model can be solved more efficiently than existing models
for the dataset with a large edge set.Comment: 21 pages, 3 figures, 4 table
Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification
Multiple kernel learning (MKL) method is generally believed to perform better
than single kernel method. However, some empirical studies show that this is
not always true: the combination of multiple kernels may even yield an even
worse performance than using a single kernel. There are two possible reasons
for the failure: (i) most existing MKL methods assume that the optimal kernel
is a linear combination of base kernels, which may not hold true; and (ii) some
kernel weights are inappropriately assigned due to noises and carelessly
designed algorithms. In this paper, we propose a novel MKL framework by
following two intuitive assumptions: (i) each kernel is a perturbation of the
consensus kernel; and (ii) the kernel that is close to the consensus kernel
should be assigned a large weight. Impressively, the proposed method can
automatically assign an appropriate weight to each kernel without introducing
additional parameters, as existing methods do. The proposed framework is
integrated into a unified framework for graph-based clustering and
semi-supervised classification. We have conducted experiments on multiple
benchmark datasets and our empirical results verify the superiority of the
proposed framework.Comment: Accepted by IJCAI 2018, Code is availabl
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