From texts, images, and data to attribute based case representation

Workshop program at the 12th Industrial Conference on Data Mining ICDM 2012 (MLDM 2012) July 13-20, 2012, Berlin/GermanyIn this article we study complex case representations in Case-Based Reasoning. To some degree this is a survey paper. But in addition it gives a unified approach to solving the problems connected with representations mentioned in the title in a way that has not been considered so far. The most popular form to represent cases use attribute-based representations. They allow an easy formulation of similarity measures and retrieval functions. However, in practical applications, case problems and solutions are in the first place given in other ways, e.g. by using texts, images, sensor data or speech data. On this level it is hard to apply reasoning and in particular CBR. This is due to the difficulty to determine similarity measures and retrieval functions. In order to overcome this we introduce a general level structure that allows to bridge the gap between bit-oriented low level and the attribute-oriented high level that is accessible to humans as well as CBR systems. The approach is put in the form of a process model

Combining case-based reasoning systems and support vector regression to evaluate the atmosphere–ocean interaction

This work presents a system for automatically evaluating the interaction that exists between the atmosphere and the ocean’s surface. Monitoring and evaluating the ocean’s carbon exchange process is a function that requires working with a great amount of data: satellite images and in situ vessel’s data. The system presented in this study focuses on computational intelligence. The study presents an intelligent system based on the use of case-based reasoning (CBR) systems and offers a distributed model for such an interaction. Moreover, the system takes into account the fact that the working environment is dynamic and therefore it requires autonomous models that evolve over time. In order to resolve this problem, an intelligent environment has been developed, based on the use of CBR systems, which are capable of handling several goals, by constructing plans from the data obtained through satellite images and research vessels, acquiring knowledge and adapting to environmental changes. The artificial intelligence system has been successfully tested in the North Atlantic Ocean, and the results obtained will be presented in this study

A CBP agent for monitoring the carbon dioxide exchange rate from satellite images

This work presents a multiagent system for evaluating automatically the interaction that exists between the atmosphere and the ocean surface. Monitoring and evaluating within the Ocean CO2 exchange process is a function requiring working with a great amount of data: satellite images and in-situ vessel’s data. The system presented in this work focuses on Ambient Intelligence (AmI) technologies since the vision of AmI assumes seamless, unobtrusive, and often invisible but also controllable interactions between humans and technology. The work presents the construction of an open multiagent architecture which, based on the use of deliberative agents incorporating case-based planning (CBP) systems, offers a distributed model for such an interaction. This work also presents an analysis and design methodology that facilitates the implementation of CBR agent based distributed artificial intelligent systems. Moreover, the architecture takes into account the fact that the working environment is dynamic and therefore it requires autonomous models that evolve over time. In order to resolve this problem an intelligent environment has been developed, based on the use of CBP-CBR agents, which are capable of handling several goals, constructing plans from the data obtained through satellite images and research vessels, acquiring knowledge and of adapting to environmental changes, are incorporated. The artificial intelligence system has been successfully tested in the North Atlantic Ocean, and the results obtained will be presented within this work

Novel Methods for Forensic Multimedia Data Analysis: Part I

The increased usage of digital media in daily life has resulted in the demand for novel multimedia data analysis techniques that can help to use these data for forensic purposes. Processing of such data for police investigation and as evidence in a court of law, such that data interpretation is reliable, trustworthy, and efficient in terms of human time and other resources required, will help greatly to speed up investigation and make investigation more effective. If such data are to be used as evidence in a court of law, techniques that can confirm origin and integrity are necessary. In this chapter, we are proposing a new concept for new multimedia processing techniques for varied multimedia sources. We describe the background and motivation for our work. The overall system architecture is explained. We present the data to be used. After a review of the state of the art of related work of the multimedia data we consider in this work, we describe the method and techniques we are developing that go beyond the state of the art. The work will be continued in a Chapter Part II of this topic

Why Case-Based Reasoning is Attractive for Image Interpretation

The development of image interpretation systems is concerned with tricky problems such as a limited number of observations, environmental influence, and noise. Recent systems lack robustness, accuracy, and flexibility

Représentation d'un environnement par un système multi-capteurs : fusion et interprétation de scène

This thesis considers the context of the possibilistic knowledge representation and processing in order to develop a scene interpretation system observed by multiple sensors. The research is focusing on a possibilistic knowledge modeling of the imperfect information provided by the sensors. Indeed, this formalism allows introducing and explicitly manipulating, within the same framework, certain imperfections carried by the collected information and also some priori knowledge about the scene, sensors, and acquisition conditions expressed by experts or resulting from observations or ambiguous judgments. This work is divided into several stages: study of the state of the art and mastery of the possibility theory, hierarchical/semantic scene analysis, study of the application of possibility theory in different semantic levels (sub-pixel, pixel, region, object and scene), and finally the application of the proposed approach for scene interpretation. The final objective of this thesis is to develop a scene interpretation system based on a possibilistic approach. This approach performs a hierarchical scene analysis relying on two processes: ascending and descending processes. The ascending process allows accumulating evidences on the existence regions (or objects), while the descending process allows calling into question, by the expert, the informational content of the regions and objects identified in the ascending process. Indeed, this system sets up the possibilistic tools allowing to jointly exploiting multiple sources of knowledge (fusion) in order to improve the interpretation of the acquired data and, thus, enriching the representation of the observed scene. The two main performed applications are segmentation/classification (ascending process) and the unmixing (descending process). The performance of the proposed system is evaluated using several types of images (synthetic images, mammographic images, MRI images, SONAR images and satellite images). The obtained results are very encouraging and show the effectiveness of possibility theory as a framework for priori knowledge representation and as a reasoning tool to extract new knowledge from available knowledge. Moreover, the proposed approach has been very effective for integrating multiple knowledge sources.Cette thèse s'inscrit dans le contexte de la représentation et du traitement possibiliste des connaissances en vue de concevoir un système d'interprétation de scène observée par de multiples capteurs. Les capteurs fournissant une ou des informations imparfaites, les recherches se sont focalisées sur une représentation possibiliste des connaissances. En effet, ce formalisme permet d'introduire et de manipuler explicitement au sein d'un même cadre, certaines imperfections portées par les informations collectées mais aussi certaines connaissances a priori sur la scène, les capteurs, les conditions d'acquisition exprimées de manière vague soit par des experts soit résultant d'observations ou de jugements ambigus. Ce travail est divisé en plusieurs étapes : état de l'art et maîtrise de la théorie des possibilités, analyse hiérarchique/sémantique de scène, étude de l'exploitation de la théorie des possibilités dans différents niveaux sémantiques (sous-pixelique, pixelique, objet et scène) et finalement, application de l'approche proposée à des fins d'interprétation de scène. L'objectif final de cette thèse est de développer un système d'interprétation de scène basé sur une démarche possibiliste. Cette approche permet d'effectuer une analyse hiérarchique de scène en se reposant sur deux processus : ascendant et descendant. Le processus ascendant permet d'accumuler l'évidence sur l'existence des régions (ou objets), tandis que le processus descendant permet de mettre en cause, par l'expert, le contenu informationnel des régions et objets identifiés dans le processus ascendant. En effet, ce système met en place les outils possibilistes nécessaires à l'exploitation conjointe de plusieurs sources de connaissances (fusion) afin d'améliorer l'interprétation des données acquises en vue d'une représentation plus riche de la scène observée : les deux principales applications visées sont donc la segmentation / classification (processus ascendant) et le démixage (processus descendant). Les performances du système d'interprétation proposé sont évaluées en utilisant plusieurs types d'images (images de synthèse, images mammographiques, images du type IRM, images SONAR, et Images satellitaires). Les résultats obtenus sont très encourageants et montrent l'efficacité de la théorie des possibilités comme un cadre de représentation des connaissances a priori et comme outil de raisonnement permettant d'extraire de nouvelles connaissances à partir des connaissances disponibles. De plus, l'approche proposée s'est montré très efficace pour l'intégration de plusieurs sources des connaissances