Regions Of Interest (ROI) are successfully used in automatic image annotation through Bag of Visual Words (BoVW) models. However, the "flat" BoVW models ignore potentially relevant topological relationships between the ROI. While several works tried to take into account such relations for Content Based Image Retrieval (CBIR), few works evaluated clearly their effects in the context of automatic image annotation. In this article, we propose to explicit the relationships between ROI in automatic annotation methods in order to evaluate their contributions on the results quality. This is done through a model, called "phrasing model", that generates groups of ROI called Visual Phrases according to relationships constraints. Based on our phrasing model, we define two kinds of Visual Phrases and we show that taking into account topological ROI relationships can significantly overcome the results of the classical BoVW model.

The paper presents a contribution to image indexing consisting in a weighting model for visible objects -- or image objects -- in home photographs. To improve its effectiveness this weighting model has been designed according to human perception criteria about what is estimated as important in photographs. Four basic hypotheses related to human perception are presented, and their validity is estimated as compared to actual observations from a user study. Finally a formal definition of this weighting model is presented and its consistence with the user study is evaluated.

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In this paper we investigate some aspects of the logical approach for retrieval models. We develop here the idea that the intrinsic expressive power of logic has already brought its benefits in information retrieval through the design of the first logical retrieval model which has proved more general, or powerful, than any existing model. This benefit lies in the expressive power, or generality of logic on one side, and in its very close fitness with the fundamentals of information retrieval. We mainly develop this second point in giving an informal interpretation of the semantics of the logical model as it was first introduced by C. J. Van Rijsbergen in 1986. This discussion is also for us an occasion to investigate in more details some less known aspects of information retrieval, as for example the impact of new applications which already induce a complete revision of the notion of document. Then the logical model is presented as the one the expressive power of which may provide a much needed common framework for a coherent integration of recent approaches in information retrieval involving the use of Natural Language Processing techniques (NLP) or Artificial Intelligence (AI) techniques. This idea is further developed in showing that the logical approach may also help in giving new insights into classical retrieval models and even help in improving them in a coherent way. An example of this later idea is developed with the Boolean model.

This paper focuses on the query processing module of RIME, an experimental prototype of an intelligent information retrieval system designed to manage high-precision queries on a corpus of medical reports. Though highly specific this particular corpus is representative of an important class of applications: information retrieval among full-text specialized documents which constitute critical sources of information in several organizations (medicine, law, space industry…). This experience allowed us to design and implement an elaborate model for the semantic content of the documents which is an extension of the Conceptual Dependency approach. The underlying retrieval model is inspired from the Logic model proposed by C.J. Van Rijsbergen, which has been considerably refined using an Extended Modal Logic. After presenting the context of the RIME project, we briefly describe the models designed for the internal representation of medical reports and queries. The main part of the paper is then devoted to the retrieval model and its application to the query processing module of RIME which has a natural language interface. Processing a query involves two main phases: the interpretation which transforms the natural language query into a search expression, and the evaluation phases which retrieves the corresponding medical reports. We focus here on the evaluation phases and show its relationship with the underlying retrieval model. Evaluations from practical experiments are also given, along with indications about current developments of the project.

This paper focuses on the RIME system aimed to the indexing of medical reports in a multimedia environment. This particular application is viewed as representative of a large set of still unanswered needs of large communities of users: domain experts dealing with on-line specialized documentation such as software engineers, medical specialists and so on. In this application textual information appears as an interesting media to access related pictures in the data base. After the presentation of the application and a study of the particular corpus involved we define a semantic model for the documents which is based on a Conceptual Language. Then we detail the indexing process and its various linguistic components which perform the translation of every medical report according to this semantic model.

IOTA is a prototype of an Information Retrieval System which can manage a corpus made of highly structured, full text documents. The first version presented here has intelligent capabilities related to heuristic pattern matching procedures for processing natural language queries, which involve an automatically built thesaurus. The paper emphazises the overall principles of query processing and gives hints about the underlying techniques used while constructing the thesaurus and automatically indexing highly structured documents.