Tabu search model selection for SVM

International audienceA model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. The aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. The selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times

Graph-based morphological processing of multivariate microscopy images and data bases

International audienceThe extension of lattice based operators to manifolds is still a challenging theme in mathematical morphology. In this paper, we propose to explicitly construct complete lattices and replace each element of a manifold by its rank suitable for classical morphological processing. Manifold learning is considered as the basis for the construction of a complete lattice. The whole processing of multivariate functions is expressed on graphs to have a formalism that can be applied on images, region adjacency graphs, and image databases. Several examples in microscopy do illustrate the benefits of the proposed approach

Color image segmentation by unsupervised 2D histogram clustering and Dempster-Shafer region merging

In this paper, a color image segmentation method based on a new approach called bimarginal is proposed.To overcome the drawbacks of the classical marginal approaches, color components are considered in pairs in order to have a partial view of their inner correlation. Working with color images, the three possible combinations are considered as three independant information sources. Each pairwise component combination is firstly analyzed according to an unsupervised morphologic clustering which looks for the dominant colors of a 2D histogram. This leads to obtain three segmentation maps combined by intersection after being simplified. The intersection process itself producing an over-segmentation of the image, a pairwise region merging is done according to a similarity criterion with the Dempster-Shafer theory up to a termination criterion. To fully automate the segmentation, an energy function is proposed to quantify the segmentation quality. The latter acts as a performance indicator and is used all over the segmentation to tune its parameters.Dans cet article nous proposons une méthode de segmentation d'images couleur selon une nouvelle approche que nous appelons bi-marginale. Afin de pallier les défauts des approches marginales classiques, nous considérons les composantes couleur deux à deux afin d'avoir une vue partielle de leur corrélation. Travaillant selon cette vision bi-composante, nous considérons les trois combinaisons possible comme trois sources d'informations indépendantes. Chaque information bi-composante est tout d'abord analysée selon un schéma de coalescence morphologique non supervisé qui recherche les couleurs dominantes d'un histogramme bidimensionnel. Cela permet de construire trois cartes de segmentation distinctes qui sont combinées par intersection après avoir été simplifiées. L'intersection produisant une sur-segmentation, une fusion des régions deux à deux est opérée selon un critère de similarité et selon la combinaison de Dempster-Shafer jusqu'à un critère de terminaison. Afin d'automatiser la méthode de segmentation, une mesure d'énergie est proposée afin de quantifier la qualité d'une segmentation, celle-ci sert tout au long de la méthode proposée comme indicateur de performance de la segmentation afin d'en régler les différents paramètres

PDEs level sets on weighted graphs

International audienceIn this paper we propose an adaptation of PDEs level sets over weighted graphs of arbitrary structure, based on PdEs and using a framework of discrete operators. A general PDEs level sets formulation is presented and an algorithm to solve such equation is described. Some transcriptions of well-known models under this formalism, as the mean-curvature-motion or active contours, are also provided. Then, we present several applications of our formalism, including image segmentation with active contours, using weighted graphs of arbitrary topologies

Lifting scheme on graphs with application to image representation

International audienceWe propose a new multiscale transform for scalar functions defined on the vertex set of a general undirected weighted graph. The transform is based on an adaption of the lifting scheme to graphs. One of the difficulties in applying directly the lifting scheme to graphs is the partitioning of the vertex set. We follow a recent greedy approach and extend it to a multilevel transform. We carefully examine each step of the algorithm, in particular its effect on the underlying basis. We finally investigate the use of the proposed transform to image representation by computing M-term nonlinear approximation errors. We provide a comparison with standard orthogonal and biorthogonal wavelet transforms

Nonlocal PdES on graphs for active contours models with applications to image segmentation and data clustering

International audienceWe propose a transcription on graphs of recent continuous global active contours proposed for image segmentation to address the problem of binary partitioning of data represented by graphs. To do so, using the framework of Partial difference Equations (PdEs), we propose a family of nonlocal regularization functionals that verify the co-area formula on graphs. The gradients of a sub-graph are introduced and their properties studied. Relations, for the case of a sub-graph, between the introduced nonlocal regularization functionals and nonlocal discrete perimeters are exhibited and the co-area formula on graphs is introduced. Finally, nonlocal global minimizers can be considered on graphs with the associated energies. Experiments show the benefits of the approach for nonlocal image segmentation and high dimensional data clustering