Karell Bertet

eBDtheque: A Representative Database of Comics

We present eBDtheque, a database of various comic book images and their ground truth for panels, balloons and text lines plus semantic annotations. The database consists of a hundred pages of various comic book albums, Franco-Belgian, American comics and mangas. Additionally, we present the piece of software used to establish the ground truth and a tool to validate results against this ground truth. Everything is publicly available for scientific use on http://ebdtheque.univ-lr.fr.

NAVIGALA: AN ORIGINAL SYMBOL CLASSIFIER BASED ON NAVIGATION THROUGH A GALOIS LATTICE

This paper deals with a supervised classification method, using Galois Lattices based on a navigation-based strategy. Coming from the field of data mining techniques, most literature on the subject using Galois lattices relies on selection-based strategies, which consists of selecting/choosing the concepts which encode the most relevant information from the huge amount of available data. Generally, the classification step is then processed by a classical classifier such as the k-nearest neighbors rule or the Bayesian classifier. Opposed to these selection-based strategies are navigation-based approaches which perform the classification stage by navigating through the complete lattice (similar to the navigation in a classification tree), without applying any selection operation. Our approach, named Navigala, proposes an original navigation-based approach for supervised classification, applied in the context of noisy symbol recognition. Based on a state of the art dealing with Galois Lattices classification based methods, including a comparison between possible selection and navigation strategies, this paper proposes a description of NAVIGALA and its implementation in the context of symbol recognition. Some objective quantitative and qualitative evaluations of the approach are proposed, in order to highlight the relevance of the method.

On the Joint Use of a Structural Signature and a Galois Lattice Classifier for Symbol Recognition

In this paper, we propose a new approach for symbol recognition using structural signatures and a Galois Lattice as classifier. The structural signatures are based on topological graphs computed from segments which are extracted from the symbol images by using an adapted Hough transform. These structural signatures, which can be seen as dynamic paths which carry high level information, are robust towards various transformations. They are classified by using a Galois Lattice as a classifier. The performances of the proposed approach are evaluated on the GREC03 symbol database and the experimental results we obtain are encouraging.

A generic description of the concept lattices' classifier: application to symbol recognition

In this paper, we present the problem of noisy images recognition and in particular the stage of primitives selection in a classification process. We suppose that segmentation and statistical features extraction on documentary images are realized. We describe precisely the use of concept lattice and compare it with a decision tree in a recognition process. From the experimental results, it appears that concept lattice is more adapted to the context of noisy images.

Weak-order extensions of an order

In this paper, at first we describe a digraph representing all the weak-order extensions of a partially ordered set and algorithms for generating them. Then we present a digraph representing all of the minimal weak-order extensions of a partially ordered set. This digraph also implies generation algorithms. Finally, we prove that the number of weak-order extensions of a partially ordered set is a comparability invariant, whereas the number of minimal weak-order extensions of a partially ordered set is not a comparability invariant.

Using Ontologies to Reduce the Semantic Gap between Historians and Image Processing Algorithms

o reduce the gap between pixel data and the-saurus semantics, this paper presents a novel approach using mapping between two ontologies on images of drop-capitals (also named drop caps or lettrines): In the first ontology, each drop cap image is endowed with semantic information describing its content. It is generated from a database of lettrines images - namely Ornamental Letter Images Data Base - manually populated by historians with drop cap images annotations. For the second ontology we have developed image processing algorithms to extract image regions on the basis of a number of features. These features, as well as spatial relations, among regions form the basis of the ontology. The ontologies are then enriched by inference rules to annotate some regions to automatically deduce their semantics. In this article, the method is presented together with preliminary experimental results and an illustrative example.

Doubling Convex Sets in Lattices: Characterizations and Recognition Algorithms

We characterize lattices obtained from another lattice by a doubling of a convex set. This gives rise to a characterization of the class CN of lattices obtained by doublings of connected and convex sets when starting from a two-element lattice, and from this characterization result we derive an efficient recognition algorithm. This algorithm can be directly applied to the recognition of lattices in the subclasses of CN defined by giving some additionnal constraints on the convex sets used in the doublings.

Lattices, closures systems and implication bases: A survey of structural aspects and algorithms

Abstract Concept lattices and closed set lattices are graphs with the lattice property. They have been increasingly used this last decade in various domains of computer science, such as data mining, knowledge representation, databases or information retrieval. A fundamental result of lattice theory establishes that any lattice is the concept lattice of its binary table. A consequence is the existence of a bijective link between lattices, contexts (via the table) and a set of implicational rules (via the canonical (direct) basis). The possible transformations between these objects give rise to relevant tools for data analysis. In this paper, we present a survey of lattice theory, from the algebraic definition of a lattice, to that of a concept lattice, through closure systems and implicational rules; including the exploration of fundamental bijective links between lattices, reduced contexts and bases of implicational rules; and concluding with the presentation of the main generation algorithms of these objects.

Symbol recognition using a concept lattice of graphical patterns

In this paper we propose a new approach to recognize symbols by the use of a concept lattice. We propose to build a concept lattice in terms of graphical patterns. Each model symbol is decomposed in a set of composing graphical patterns taken as primitives. Each one of these primitives is described by boundary moment invariants. The obtained concept lattice relates which symbolic patterns compose a given graphical symbol. A Hasse diagram is derived from the context and is used to recognize symbols affected by noise. We present some preliminary results over a variation of the dataset of symbols from the GREC 2005 symbol recognition contest.

Graphic Recognition: The Concept Lattice Approach

Object recognition is a very large problem that can be derived in different forms. In the domain of graphic recognition, many strategies are proposed, but many of them depend on the context in which they are applied [LVSM01]. This aspect implies the necessity to find a model for this context, and to use it for the implementation of dynamic and adaptative systems. In this paper, we focus on the object recognition problem where a knowledge base defined by a finite set of representative prototypes or class objects is given.