Zeina Abu-Aisheh

Zeina Abu-Aisheh
University of Caen Normandy | UNICAEN · Département d'Informatique

PhD - Researcher

About

17
Publications
4,576
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401
Citations

Publications

Publications (17)
Chapter
The graph Laplacian plays an important role in describing the structure of a graph signal from weights that measure the similarity between the vertices of the graph. In the literature, three definitions of the graph Laplacian have been considered for undirected graphs: the combinatorial, the normalized and the random-walk Laplacians. Moreover, a no...
Article
The k-nearest neighbors classifier has been widely used to classify graphs in pattern recognition. An unknown graph is classified by comparing it to all the graphs in the training set and then assigning it the class to which the majority of the nearest neighbors belong. When the size of the database is large, the search of k-nearest neighbors can b...
Article
Graph Distance Contest (GDC) was organized in the context of ICPR 2016. Its main challenge was to inspect and report performances and effectiveness of exact and approximate graph edit distance methods by comparison with a ground truth. This paper presents the context of this competition, the metrics and datasets used for evaluation, and the results...
Article
Graph edit distance (GED) has emerged as a powerful and flexible graph matching paradigm that can be used to address different tasks in pattern recognition, machine learning, and data mining. GED is an error-tolerant graph matching problem which consists in minimizing the cost of the sequence that transforms a graph into another by means of edit op...
Article
In this paper, a new binary linear programming formulation for computing the exact Graph Edit Distance (GED) between two graphs is proposed. A fundamental strength of the formulations lies in their genericity since the GED can be computed between directed or undirected fully attributed graphs. Moreover, a continuous relaxation of the domain constra...
Conference Paper
Full-text available
This paper is an exhibition of graph matching results, in a classification context. We present Photo(Graph) Gallery, a platform that allows one to visually interpret graph matchings. We aim at under- standing the computed matchings in order to improve the rates of graph classification. Preliminary results of the study performed on two data sets are a...
Conference Paper
When using k-nearest neighbors, an unknown object is classified by comparing it to all the prototypes stored in the training database. When the size of the database is large, and especially if prototypes are represented by graphs, the search of k-nearest neighbors can be very time consuming. On this basis, some researchers have tried to propose opt...
Conference Paper
This paper presents a binary linear program which computes the exact graph edit distance between two richly attributed graphs (i.e. with attributes on both vertices and edges). Without solving graph edit distance for large graphs, the proposed program enables to process richer and larger graphs than existing approaches based on mathematical program...
Article
Full-text available
Due to the inherent genericity of graph-based representations, and thanks to the improvement of computer capacities, structural representations have become more and more popular in the field of Pattern Recognition (PR). In a graph-based representation, vertices and their attributes describe objects (or part of them) while edges represent interrelat...
Article
In this paper, we propose and explain the use of anytime algorithms in graph matching (GM). GM methods have been involved in many pattern recognition problems. In such a context, GM methods are part of a more complex retrieval system that imposes time and memory constraints on such methods. Anytime algorithms are well suited for use in such an unce...
Conference Paper
Graph edit distance is an error tolerant matching technique that can be used efficiently to address different tasks in pattern recognition, machine learning, and data mining. The literature is rich of many fast heuristics with unbounded errors but few works are devoted to optimal graph edit distance computation. Optimal graph edit distance methods...
Thesis
En raison de la capacité et de l'amélioration des performances informatiques, les représentations structurelles sont devenues de plus en plus populaires dans le domaine de la reconnaissance de formes (RF). Quand les objets sont structurés à base de graphes, le problme de la comparaison d'objets revient à un problme d'appariement de graphes (Graph M...
Conference Paper
Graph edit distance (GED) is an error tolerant graph matching paradigm whose methods are often evaluated in a classification context and less deeply assessed in terms of the accuracy of the found solution. To evaluate the accuracy of GED methods, low level information is required not only at the classification level but also at the matching level....
Conference Paper
Full-text available
Graph edit distance is an error tolerant matching technique emerged as a powerful and flexible graph matching paradigm that can be used to address different tasks in pattern recognition, machine learning and data mining; it represents the minimum-cost sequence of basic edit operations to transform one graph into another by means of insertion, delet...
Conference Paper
Web design galleries are extremely popular for searching inspiration in web design, but there is a lack of rich search functions. Recent works in the field have focused on style similarity browsing, where one hops from design to design based on their style similarity. In this paper, we claim for a study of the multiple dimensions of this notion of...

Questions

Questions (2)
Question
I want to write a code for Switching Linear Dynamical System with an extended version of kalman filter (assumed density function) in Python, which library do you recommend me to use? Pymc3 or something else? Is Pymc3 adaptable for online inference? Any hint could be appreciated
Question
I know that Hadoop MapReduce divides the big problem into independent smaller problems. But will it be possible to share information between map workers (for example if someone found a better upper bound, how can this map worker share such information with the other map workers?)?

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