Résumé: In the context of MultiCriteria Decision Aid, we present new properties of a 2-additive bi-capacity by using a bipolar M¨obius transform. We use these properties in the identification of a 2-additive bi-capacity when we represent a cardinal information by a Choquet integral with respect to a 2-additive bi-capacity.

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Résumé: In the context of MultiCriteria Decision Aid, we present new properties of a 2-additive bi-capacity by using a bipolar M¨obius transform. We use these properties in the identification of a 2-additive bi-capacity when we represent a cardinal information by a Choquet integral with respect to a 2-additive bi-capacity.

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Résumé: The goal of this paper is to introduce a set of techniques we call semantic combination in order to efficiently fuse text and image retrieval systems in the context of multimedia information access. These techniques emerge from the observation that image and textual queries are expressed at different semantic levels and that a single image query is often ambiguous. Overall, the semantic combination techniques overcome a conceptual barrier rather than a technical one: these methods can be seen as a combination of late fusion and image reranking. Albeit simple, this approach has not been used yet. We assess the proposed techniques against late and cross-media fusion using 4 different ImageCLEF datasets. Compared to late fusion, performances significantly increase on two datasets and remain similar on the two other ones.

DOI: 10.1007/978-3-642-18449-9_10
Résumé: Information seeking in a multimedia database very often implies a search process that is complex, dynamic and multi-faceted. Moreover the information need with respect to a topic is likely to evolve during the same search session, going from a simple lookup search to a thorough discovery of connected subtopics. We propose a system that aims at addressing these challenges. It couples serendipitous browsing and query-based search in a smooth manner. The proposed system offers two levels, one global and one local, of visualizing the context of the information seeking task and it also allows to view and search the data using either monomodal or cross-modal similarities. Furthermore, the system integrates a new relevance feedback model that takes into account the multimodal nature of the data in a flexible way and a combination of two parameters, the locality and forgetting factors, that allows the user to design adaptive metrics in the interactive search process. The paper also presents a preliminary user-centered evaluation of our system and concludes with an analysis of the evaluation results.

Commentaires: jap_al-amr09_lncs - Revised Selected Papers

DOI: 10.1007/978-3-642-15751-6_13
Résumé: The aim of this paper is to analyze the technologies designed and used in the context of XRCE’s participation in the Photo Retrieval Task of ImageCLEF 2009 [1]. We evaluate and compare different mono and multimedia retrieval methods and two distinct diversity-seeking strategies as well. Our analysis allows us to better understand which combinations of basic approaches are the best ones. It appears that taking advantage of the multimodal nature of the data by means of our cross-modal similarities technique and leveraging different text representations of the topics in the goal of covering distinct related subtopics, allow us to tackle the Photo Retrieval Task effectively.

Commentaires: jap_al-clef09_proc

DOI: 10.3233/WIA-2011-0204
Résumé: This paper is concerned with the problem of unsupervised rank aggregation in the context of metasearch in information retrieval. In such tasks, we are given many partial ordered lists of retrieved items provided by many search engines and we want to define a way for aggregating those lists in order to find out a consensus. One classical approach consists in aggregating, for each retrieved item, the scores given by the different search engines. Then, we use the resulting aggregated scores distribution in order to infer a consensus ordered list. In this paper we investigate whether aggregation operators defined in the fields of multi-sensor fusion and multicriteria decision making are of interest for metasearch problems or not. Moreover, another purpose of this paper is to introduce a new aggregation operator, its foundations and its properties. We finally test all these aggregation operators for metasearch tasks using the Letor 2.0 dataset. Our results show that among the studied aggregation functions, the ones which are more compensatory outperform the baseline methods CombSUM and CombMNZ.

Commentaires: jap_al-wias10