Ali Idarrou

Review: Automatic Image Annotation for Semantic Image Retrieval

Nowadays, the number of digital data sets grows exponentially. Hence, the need to conceive efficient and powerful image indexation and retrieval systems grows as well. Automatic image annotation was adopted by several research as the emerging trend in image retrieval area. Actually, it is considered as the best solution that combines the content-based techniques by using low-level image features and text-based techniques exploiting textual annotations, associated to the image. In this way, the semantic gap between low-level image features and high-level semantics will be reduced. This paper presents a review of image retrieval approaches, by focusing especially on the automatic image annotation methods, in order to analyse the impact of annotations and associating semantics to the visual data for an image retrieval process.

Detecting Communities by Integrating Perspective Communities as Semantic Information

A set of actors may take part in numerous categories of real-life social networks. For instance an actor may take part in a mobile social network and a friendship network. The interactions between actors in the mobile social network may not be reproduced in the friendship one. We accept that overlaying networks with different kinds of interactions assists on detecting interesting patterns. The main goal of this paper is to overlay two or many social networks with different kinds of social activities in order to mine consistent communities that could not be appeared in a single social network. In this respect, we introduce a new community detection approach based on injecting the perspective communities from a mobile social network into an initial friendship network within a sequence of time windows. In other words, during each time window, we constructed an augmented friendship social network using the semantic information extracted from perspective communities and from it to detect communities that may belong to several networks in a given time period. We demonstrate the abilities and potential of our approach by evaluating it using real datasets and comparing it with two previous methods.

The impact of perspective communities on information flow in social networks

Social networks are dynamic topologies with a set of nodes playing a significant role in the information flow transmission within the network. The appearance of new links in the network may change the role of nodes and conduct to a rapid information flow. For example an individual may belong to an online social network and a friendship network. The relationships between individuals in the mobile social network may not be reflected in the friendship one. We have confidence that overlapping networks with several kinds of interactions provide a fast propagation of information and helps to identify critical and/or non-critical nodes. This article aims to extend our previous work by injecting the perspective communities from a mobile social network from a sequence of time windows in a friendship social network. Centrality and distance measures were applied to validate the impact of perspectives communities on the quality of information flow.

Multi-ontology based semantic annotation review

Day by day, the amount of digital data grows exponentially. Furthermore, these data come from heterogeneous content and structure resources. Both factors are the major handicap that limits the correct functioning of information systems. Being on the same wavelength, researchers have fixed as a challenge to reach targeted information retrieval which allows an easy access to the desired resource then return relevant and correct outcomes corresponding to a specific request. Therefore, semantic annotation is chosen as a paramount solution that may handle all these requirements, while ensuring a good understanding of document content then exploit and share it readily afterward. In this paper, we treat semantic annotation of documents using multi-ontologies, by stressing the effect of that on the relevance and the quality of semantic annotation results. We present a survey of different multi-ontologies based semantic annotation approaches and conclude with a synthesis of the study made.

Structural Clustering Multimedia Documents: An Approach based on Semantic Sub-graph Isomorphism

The works that used graphs to represent documents has referred to the richness of these expressive tools. However, the exploited graph theory could be of great interest concerning the evaluation of similarity between these documents, both in documentary classification and the information retrieval. In structural classification of the documents, object of this work, the similarity measure is a crucial step. In many applications, this step results in a subgraph isomorphism problem. This problem is known in graph theory by a combinatorial explosion. To get around this problem, we propose to consider a graph as a set of paths that compose it. The matching, paths allows reducing the combinatorial cost. We propose a structural measure based on the sub-graph isomorphism and we discuss the quality of our classifier, especially the separation of classes. We’d like to show that our measure is structural, not a “surface measure” and evaluate our approach on a corpus of multimedia documents extracted, randomly, from the INEX 2007 corpus.

Classification of multi-structured documents: a comparison based on media image

This paper focuses on the structural comparison of multimedia documents. Most of the systems treating the multimedia documents exploit only the text part of these documents. However, the text is no longer the only means to carry information. The major issue is to extend these systems to the other modality notably to the image that constitutes one of the basic components of multimedia documents. The complexity of multimedia documents, multistructured in essence, imposes not only a structural representation in the form of trees, but rather in the form of graphs. The graphs are in appropriateness to the description of these documents. For example, one will be able to describe the components of a scene of an image, the relations between these components, their positions (spatial relations), etc. We propose a new similarity measure of graphs, based on a univocal matching between the graphs to compare. In our approach, we will take account of structural information and specificities of multimedia information. We evaluate our measure on a corpus of multi-structured documents from the INEX 2007 corpus.