Frequent Graph Discovery: Application to Line Drawing Document Images

International audienceIn this paper a sequence of steps is applied to a graph representation of line drawings using concepts from data mining. This process finds frequent subgraphs and then association rules between these subgraphs. The distant aim is the automatic discovery of symbols and their relations, which are parts of the document model. The main outcome of our work is firstly an algorithm that finds frequent subgraphs in a single graph setting and secondly a modality to find rules and meta-rules between the discovered subgraphs. The searched structures are closed [1] and disjunct subgraphs. One aim of this study is to use the discovered symbols for classification and indexation of document images when a supervised approach is not at hand. The relations found between symbols can be used in segmentation of noisy and occluded document images. The results show that this approach is suitable for patterns, symbols or relation discovery

Frequent Graph Discovery : application to Line Drawing Document Images

In this paper a sequence of steps is applied to a graph representation of line drawings using concepts from data mining. This process finds frequent subgraphs and then association rules between these subgraphs. The distant aim is the automatic discovery of symbols and their relations, which are parts of the document model. The main outcome of our work is firstly an algorithm that finds frequent subgraphs in a single graph setting and secondly a modality to find rules and meta-rules between the discovered subgraphs. The searched structures are closed [1] and disjunct subgraphs. One aim of this study is to use the discovered symbols for classification and indexation of document images when a supervised approach is not at hand. The relations found between symbols can be used in segmentation of noisy and occluded document images. The results show that this approach is suitable for patterns, symbols or relation discovery

Contribution aux méthodes de reconnaissance structurelle de formes (approche à base de projections de graphes)

Les travaux exposés dans cette thèse portent sur une contribution aux techniques de projection de graphes, appliquées à la reconnaissance de formes, visant à tirer parti de la richesse des méthodes structurelles et de l efficacité des outils statistiques. Nous présentons une nouvelle projection s inscrivant dans la catégorie des sondages de graphes. La première contribution de cette thèse porte sur l encapsulation de la topologie du graphe dans une représentation vectorielle, en s appuyant sur le dénombrement de motifs (sous-graphes) issus d un lexique généré indépendamment du contexte. Ces motifs permettent de minimiser les pertes de l information topologique lors de la projection. La deuxième contribution porte sur l intégration de l information relative aux étiquettes au sein de notre projection par l adjonction de leurs dénombrements. Aux problèmes liés à la nature et la variabilité des attributs, nous proposons deux solutions dans le but de constituer des classes d étiquettes moins nombreuses. La première consiste à discrétiser les attributs numériques puis à les combiner. La deuxième vise à former ces classes par un partitionnement global de l ensemble des étiquettes. Ces propositions sont ensuite évaluées sur différentes bases de graphes et dans différents contextes.The work exposed in this thesis focuses on a contribution to techniques of graph embedding, applied to pattern recognition, aiming to take advantages of the richness of structural methods and the efficiency of statistical tools. We present a new embedding, joining the category of graph probing. The first contribution of this thesis deals with the embedding of the graph topology in a vectorial representation, based on the counting of patterns (subgraphs) stemming of a lexicon generated independently of the context. These patterns permit the minimization of losses of the topological information during the embedding. The second contribution focuses on the integration of the information related to labels inside our embedding by adding their counting. To deal with problems linked to the nature and the variability of the attributes, we suggest two solutions to reduce the number of label classes. The first one consists of discretizing numeral attributes and combining them The second one aims to build these classes by a global clustering on the set of labels. Then, these proposals are evaluated on different datasets of graphs and in different contexts.TOURS-Bibl.électronique (372610011) / SudocSudocFranceF

Urinary And Breast Milk Biomarkers To Assess Exposure Ro Naphthalene In Pregnant Women: An Investigation Of Personal And Indoor Air Sources

Naphthalene exposures for most non-occupationally exposed individuals occur primarily indoors at home. Residential indoor sources include pest control products (specifically moth balls), incomplete combustion such as cigarette smoke, woodstoves and cooking, some consumer and building products, and emissions from gasoline sources found in attached garages. The study aim was to assess naphthalene exposure in pregnant women from Canada, using air measurements and biomarkers of exposure

Allosteric modulation of metabotropic glutamate receptor 4 activates IDO1-dependent, immunoregulatory signaling in dendritic cells

Metabotropic glutamate receptor 4 (mGluR4) possesses immune modulatory properties in vivo, such that a positive allosteric modulator (PAM) of the receptor confers protection on mice with relapsing-remitting experimental autoimmune encephalomyelitis (RR-EAE). ADX88178 is a newly-developed, one such mGluR4 modulator with high selectivity, potency, and optimized pharmacokinetics. Here we found that application of ADX88178 in the RR-EAE model system converted disease into a form of mild-yet chronic-neuroinflammation that remained stable for over two months after discontinuing drug treatment. In vitro, ADX88178 modulated the cytokine secretion profile of dendritic cells (DCs), increasing production of tolerogenic IL-10 and TGF-β. The in vitro effects required activation of a Gi-independent, alternative signaling pathway that involved phosphatidylinositol-3-kinase (PI3K), Src kinase, and the signaling activity of indoleamine 2,3-dioxygenase 1 (IDO1). A PI3K inhibitor as well as small interfering RNA targeting Ido1-but not pertussis toxin, which affects Gi protein-dependent responses-abrogated the tolerogenic effects of ADX88178-conditioned DCs in vivo. Thus our data indicate that, in DCs, highly selective and potent mGluR4 PAMs such as ADX88178 may activate a Gi-independent, long-lived regulatory pathway that could be therapeutically exploited in chronic autoimmune diseases such as multiple sclerosis

Structural Classification for Retrospective Conversion of Documents

This paper describes the structural classi cation method used in a strategy for retrospective conversion of documents. This strategy consists in an cycle in which document analysis and document understanding interact. This cycle is initialized by the extraction of the outline of the layout and logical structures of the document. Then, each iteration of the cycle consists in the detection and the processing of inconsistencies in the document modeling. The cycle ends when no more inconsistency occurs