12 research outputs found

    Analyse et organisation de corpus pour une recherche thematico-visuelle d'images

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    Colloque avec actes et comitĂ© de lecture.La recherche d'images dans de grandes bases d'images suppose des capacitĂ©s d'interrogation fondĂ©es Ă  la fois sur le contenu visuel et thĂ©matique. Un lourd travail d'indexation manuelle des images est un prĂ©alable Ă  toute tentative d'interrogation "rĂ©aliste" de la base, alors que les interrogations fondĂ©es uniquement sur le contenu visuel peuvent exploiter les techniques automatiques d'analyse d'images. Il nous semble que les deux approches peuvent ĂȘtre efficacement combinĂ©es, de maniĂšre Ă  permettre au systĂšme de retrouver des documents thĂ©matiquement et visuellement pertinents, et Ă©galement d'autoriser le traitement de bases qui ne sont pas totalement indexĂ©es thĂ©matiquement. Dans cet article, nous prĂ©sentons un systĂšme rĂ©alisant une telle intĂ©gration grĂące Ă  une prĂ©-organisation du corpus, qui agit Ă  la fois sur les caractĂ©ristiques thĂ©matiques et visuelles des images. Un processus de recherche adaptĂ© Ă  cette organisation est proposĂ© ; il privilĂ©gie l'interaction avec l'utilisateur par le biais d'un bouclage de pertinence thĂ©matique et visuel. Nous exposons Ă©galement les rĂ©sultats de nos expĂ©rimentations et quelques pistes d'approfondissement de notre approche

    An Alternative Image Retrieval System Based on Visual and Thematic Corpus Organisation

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    Colloque avec actes et comité de lecture.Retrieving images in large databases involves either thematic or visual querying capabilities. We think that both approaches can be efficiently combined to provide a thematically and visually relevant retrieval. This paper deals with bi-modal access in image retrieval systems. Our approach relies on a corpus organisation based on both visual and thematical clustering, allowing access to non-indexed images. An adapted retrieval process has been designed to reformulate queries, taking into account users's judgements that deal with thematical and visual aspects of their information needs

    Thematico-Visual Image Retrieval: How to Deal With Partially Indexed Corpora

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    Colloque avec actes et comité de lecture. internationale.International audienceIt becomes very easy to access large amounts of images when surfing the Internet. All images, however, are not always thematically indexed. We think that partially thematically indexed corpora can be organised in a way that facilitates retrieval. We assume that, concerning visual properties, the corpus is totally indexed by means of ``generic'' features. Based on these indexes, a hierarchical clustering technique is used to bring together images that share some similarities: two distinct structures are built (``dendrograms''). We propose a new retrieval strategy based on a virtual image that captures the user's need along the retrieval session, taking into account both thematic and visual aspects. Clusters are successively selected in each dendrogram. A combined method, called tunnels, allows dendrograms cooperation. Images are then ranked according to the virtual image. After each retrieval step, the virtual image is enriched within a relevance feedback process. Theme, colour and general layout of each images can be rated and the query is updated accordingly. In our experiments, we used two different corpora (2470 and 1100 images) to assess the performance of our thematico-visual approach within different indexing conditions. Experimentation results confirm the relevance of our approach and suggests improvement possibilities

    Gestion et qualité de l'information stratégique : une approche par les risques des systÚmes décisionnels.

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    National audienceL'aide à la prise de décision est aujourd'hui en partie réalisée grùce aux entrepÎts de données, qui peuvent intégrer une grande quantité de données provenant aussi bien de l'entreprise que de son environnement. Cependant, la diversité et la complexité des données, des processus et des acteurs impliqués dans ce dispositif technique nous invitent à prendre en compte des risques nouveaux (comme, par exemple, la non qualité des données, la non détection de changements significatifs, etc.). Face à ces risques, il est souhaitable de concevoir un systÚme plus proche de ses utilisateurs, capable d'assister leurs recherches ou leurs manipulations d'informations, de les conseiller, ou encore de les alerter à bon escient. Dans cette optique, nous proposons une approche par les risques qui permet, selon nous, une prise en compte complÚte et trÚs spécifique du problÚme de l'assistance à l'utilisateur dans le cade d'un SystÚme Décisionnel. Nous en déduisons l'architecture générale d'un systÚme collaboratif d'aide à la décision centré sur les besoins spécifiques de tous les acteurs du systÚme décisionnel, lequel devient une sorte d'atelier de travail personnalisable. || Decision making is often based on data warehouses, that store a large amount of data, coming from either inside or outside the organisation. However, the complexity and diversity of data, processes and actors involved require management of new kinds of risks (eg data quality, meaningful signal detection failures, etc.). To face these risks, a system that takes into account more of users' needs is to be devised: query and manipulation assistance, advices or alert generation are some of the intended features we foresee. We propose a risk-based approach that can, according to us, provide a comprehensive and specific answer to the users' assistance problem. We provide the architecture of such a collaborative, user-centric, decision support system, as a customisable data workbench

    A Novel Approach for Accessing Partially Indexed Image Corpora

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    Colloque avec actes et comité de lecture. internationale.International audienceThis paper addresses the issue of efficient retrieval from image corpora in which only a little proportion is thematically indexed. We propose a hybrid approach integrating thematic querying/search with content-based retrieval. We show how a preliminary double clustering of image corpus exploited by an adapted retrieval process constitutes an answer to the pursued objective. The retrieval process takes advantage of user-system interaction via relevance feedback mechanism whose results are integrated in a {\it virtual image}. Some experimental results are provided and discussed to demonstrate the effectiveness of this work

    Organising and Searching Partially Indexed Image Databases

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    Colloque avec actes et comité de lecture. internationale.International audienceThis paper addresses the issue of efficient retrieval from image corpora in which only a little proportion is textually indexed. We propose a hybrid approach integrating textual search with content-based retrieval. We show how a preliminary double clustering of image corpus exploited by an adequate retrieval process constitutes an answer to the pursued objective. The retrieval process takes advantage of user-system interaction via relevance feedback mechanism whose results are integrated in a virtual image. Experimental results on the PICAP prototype are reported ed and discussed to demonstrate the effectiveness of this work

    Etude des facteurs de risque pour la prise de décision en intelligence économique : une approche cognitive

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    La prise de dĂ©cision est un processus pris en charge par une personne, un ensemble de personnes, ou des groupes de personnes ou des entreprises. Il a Ă©tĂ© Ă©tabli que c'est un processus « vivant » : toute forme de prise de dĂ©cision a des consĂ©quences qui dĂ©terminent le succĂšs ou l'Ă©chec d'autres actes. Ce processus peut ĂȘtre simple (dĂ©cisions personnelles) ou complexe (dĂ©cisions impliquant de grandes organisations ou des gouvernements), le poids attachĂ© Ă  ces dĂ©cisions, exprimĂ© sous forme de risque, varie de la mĂȘme façon. La connaissance humaine s'enrichit d'expĂ©riences et de capacitĂ©s de raisonnement qui permettent d'ordonner une masse d'informations disponibles. Dans ce contexte, il est impĂ©ratif que les dĂ©cisions qui rĂ©sultent de ce processus soient mises sous contrĂŽle. La capacitĂ© de dĂ©cision, que nous nommerons « dĂ©cisionnabilitĂ© », est dĂ©terminĂ©e par de nombreux facteurs incluant notamment le jugement, l'expĂ©rience, les capacitĂ©s cognitives. Les dĂ©cisions Ă©tant prises Ă  partir d'informations, le mode et la mĂ©thode permettant d'obtenir ces informations sont aussi importants que la dĂ©cision elle-mĂȘme. Il s'avĂšre que la formulation et la mise en Ɠuvre du processus de dĂ©cision a un impact important sur le rĂ©sultat de la dĂ©cision, et ceci peut ĂȘtre mis en relation avec les capacitĂ©s cognitives du dĂ©cideur et des facteurs de risques. La prise de dĂ©cision, en vue d'amĂ©liorer leur performance organisationnelle, est le point focal de l'intelligence Ă©conomique

    Decisionability: contending with information flow, information quality, and information, overload in economic intelligence

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    International audienceThe importance of appropriate and adequate information toward decision making cannot be overemphasized. Considering the sheer volume of available information to information consumer and the rapidly advancing information management technologies facilitating corporate firms and organization to manage large and complex data sources aimed at exploring new opportunities towards strategic decision making. The age of inadequate information is gradually fading off while unconsciously introducing another concept known as “Information Overload”. Using adequate and timely available information for delivering strategic decisions is the focus of economic intelligence, however with ever increasing volume of information made available to the decision maker, the risk of indecision, forgetfulness, amongst others result. The consequent could be disastrous as the need arises for decision based on the interrelationship amongst the trio of decision situation, decision maker and the decision process in the face of information overload. The importance for knowledge reconciliation as the first step before information search has been proposed, this work attempts to establish the need for usage of ‘fine-tuned' information in taking strategic decisions

    Ontological Framework for Minimizing the Risk of Non-Quality Data During Knowledge Reconciliation in Economic Intelligence Process

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    International audienceKnowledge is seen as legitimate and meaningful resources that strengthens the overall management performance. As a result, knowledge management is viewed as a sine qua non towards creation, storage, sharing, and reusing of the organization's knowledge, employing advances in today's technology. Economic Intelligence (EI) is saddled with usage of timely availability of information towards strategic decision making. However, while decision making is based on available information, it has been observed with concern that reconciling the “need for decision” and subsequent “search for relevant information” poses a serious threat to the overall decision because of some intangible factor that are difficult to be expressed culminating into non-quality of retrieved data, and sometime time taken to adequately mapped the decision maker's “mind-set” into an appropriate object for information retrieval. Ontology potentially enable automated knowledge sharing and reuse among both human and computer agents; this is facilitated based on their ability to interweave human and machine understanding through formal and real-world semantics
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