Automatic difference measure between movies using dissimilarity measure fusion and rank correlation coefficients

International audienceWhen considering multimedia database growth, one current challenging issue is to design accurate navigation tools. End user basic needs, such as exploration, similarity search and favorite suggestions, lead to investigate how to find semantically resembling media. One way is to build numerous continuous dissimilarity measures from low-level image features. In parallel, an other way is to build discrete dissimilarities from textual information which may be available with video sequences. However, how such different measures should be selected as relevant and be fused ? To this aim, the purpose of this paper is to compare all those various issimilarities and to propose a suitable ranking fusion method for several dissimilarities. Subjective tests with human observers on the CITIA animation movie database have been carried out to validate the model

Structuration de bases multimédia pour une exploration visuelle

The large increase in multimedia data volume requires the development of effective solutions for visual exploration of multimedia databases. After reviewing the visualization process involved, we emphasis the need of data structuration. The main objective of this thesis is to propose and study clustering and classification of multimedia database for their visual exploration.We begin with a state of the art detailing the data and the metrics we can produce according to the nature of the variables describing each document. Follows a review of the projection and classification techniques. We also present in detail the Spectral Clustering method.Our first contribution is an original method that produces fusion of metrics using rank correlations. We validate this method on an animation movie database coming from an international festival. Then we propose a supervised classification method based on rank correlation. This contribution is evaluated on a multimedia challenge dataset. Then we focus on Spectral Clustering methods. We test a supervised Spectral Clustering technique and compare to state of the art methods. Finally we examine active semi-supervised Spectral Clustering methods. In this context, we propose and validate constraint propagation techniques and strategies to improve the convergence of these active methods.La forte augmentation du volume de données multimédia impose la mise au point de solutions adaptées pour une exploration visuelle efficace des bases multimédia. Après avoir examiné les processus de visualisation mis en jeu, nous remarquons que ceci demande une structuration des données. L’objectif principal de cette thèse est de proposer et d’étudier ces méthodes de structuration des bases multimédia en vue de leur exploration visuelle.Nous commençons par un état de l’art détaillant les données et les mesures que nous pouvons produire en fonction de la nature des variables décrivant les données. Suit un examen des techniques de structuration par projection et classification. Nous présentons aussi en détail la technique du Clustering Spectral sur laquelle nous nous focaliserons ensuite.Notre première réalisation est une méthode originale de production et fusion de métriques par corrélation de rang. Nous testons cette première méthode sur une base multimédia issue de la vidéothèque d’un festival de films. Nous continuons ensuite par la mise au point d’une méthode de classification supervisée par corrélation que nous testons avec les données vidéos d’un challenge de la communauté multimédia. Ensuite nous nous focalisons sur les techniques du Clustering Spectral. Nous testons une technique de Clustering Spectral supervisée que nous comparons aux techniques de l’état de l’art. Et pour finir nous examinons des techniques du Clustering Spectral semi-supervisé actif. Dans ce contexte, nous proposons et validons des techniques de propagation d’annotations et des stratégies permettant d’améliorer la convergence de ces méthodes de classement

Deep Learning vs Spectral Clustering into an active clustering with pairwise constraints propagation

International audienceIn our data driven world, categorization is of major importance to help end-users and decision makers understanding information structures. Supervised learning techniques rely on annotated samples that are often difficult to obtain and training often overfits. On the other hand, unsupervised clustering techniques study the structure of the data without disposing of any training data. Given the difficulty of the task, supervised learning often outperforms unsupervised learning. A compromise is to use a partial knowledge, selected in a smart way, in order to boost performance while minimizing learning costs, what is called semi-supervised learning. In such use case, Spectral Clustering proved to be an efficient method. Also, Deep Learning outperformed several state of the art classification approaches and it is interesting to test it in our context. In this paper, we firstly introduce the concept of Deep Learning into an active semi-supervised clustering process and compare it with Spectral Clustering. Secondly, we introduce constraint propagation and demonstrate how it maximizes partitioning quality while reducing annotation costs. Experimental validation is conducted on two different real datasets. Results show the potential of the clustering methods

Semi-supervised spectral clustering with automatic propagation of pairwise constraints

International audienceIn our data driven world, clustering is of major importance to help end-users and decision makers understanding information structures. Supervised learning techniques rely on ground truth to perform the classification and are usually subject to overtraining issues. On the other hand, unsupervised clustering techniques study the structure of the data without disposing of any training data. Given the difficulty of the task, unsupervised learning tends to provide inferior results to supervised learning. To boost their performance, a compromise is to use learning only for some of the ambiguous classes. In this context, this paper studies the impact of pairwise constraints to unsupervised Spectral Clustering. We introduce a new generalization of constraint propagation which maximizes partitioning quality while reducing annotation costs. Experiments show the efficiency of the proposed scheme

Structuring multimedia bases for visual exploration

La forte augmentation du volume de données multimédia impose la mise au point de solutions adaptées pour une exploration visuelle efficace des bases multimédia. Après avoir examiné les processus de visualisation mis en jeu, nous remarquons que ceci demande une structuration des données. L’objectif principal de cette thèse est de proposer et d’étudier ces méthodes de structuration des bases multimédia en vue de leur exploration visuelle.Nous commençons par un état de l’art détaillant les données et les mesures que nous pouvons produire en fonction de la nature des variables décrivant les données. Suit un examen des techniques de structuration par projection et classification. Nous présentons aussi en détail la technique du Clustering Spectral sur laquelle nous nous focaliserons ensuite.Notre première réalisation est une méthode originale de production et fusion de métriques par corrélation de rang. Nous testons cette première méthode sur une base multimédia issue de la vidéothèque d’un festival de films. Nous continuons ensuite par la mise au point d’une méthode de classification supervisée par corrélation que nous testons avec les données vidéos d’un challenge de la communauté multimédia. Ensuite nous nous focalisons sur les techniques du Clustering Spectral. Nous testons une technique de Clustering Spectral supervisée que nous comparons aux techniques de l’état de l’art. Et pour finir nous examinons des techniques du Clustering Spectral semi-supervisé actif. Dans ce contexte, nous proposons et validons des techniques de propagation d’annotations et des stratégies permettant d’améliorer la convergence de ces méthodes de classement.The large increase in multimedia data volume requires the development of effective solutions for visual exploration of multimedia databases. After reviewing the visualization process involved, we emphasis the need of data structuration. The main objective of this thesis is to propose and study clustering and classification of multimedia database for their visual exploration.We begin with a state of the art detailing the data and the metrics we can produce according to the nature of the variables describing each document. Follows a review of the projection and classification techniques. We also present in detail the Spectral Clustering method.Our first contribution is an original method that produces fusion of metrics using rank correlations. We validate this method on an animation movie database coming from an international festival. Then we propose a supervised classification method based on rank correlation. This contribution is evaluated on a multimedia challenge dataset. Then we focus on Spectral Clustering methods. We test a supervised Spectral Clustering technique and compare to state of the art methods. Finally we examine active semi-supervised Spectral Clustering methods. In this context, we propose and validate constraint propagation techniques and strategies to improve the convergence of these active methods

« Assessing possible changes in a town’s buildings – Fuzzy logic and 3D simulation Applied to the city of Nice »

ECTQG 2013This paper presents an approach aiming to anticipate possible transformations of the overall built fabric of a town, analysed at building level. The research is applied to the city of Nice. A grid with 400m X 400m cells is applied over the commune's area. The field of study is made up of 163 cells concentrating half of the built-up area. The possibilities of transformation of buildings within a cell are estimated starting from hypotheses relating to four groups of variable: the structure of the built fabric, attractiveness linked to an infrastructure programme planned in the cell or nearby, the town's policy (urban renovation and climate plan) and residents' sensitivity to measures and practices in favour of sustainable development. The method consists in estimating the potential of change of the built fabric in each cell by means of fuzzy logic. We use fuzzy Inference Systems to address the problem of future spatial changes. Data are either quantitative or qualitative, in view of the fact that it is often not possible to describe the environmental and social properties (e.g. architectural quality, social acceptance…) using objective indicators. Thereby, the spatial knowledge is described by semantic variables often affected by a high degree of inaccuracy and uncertainty, making fuzzy logic a suitable tool for expressing and processing this information. The potential of change is assessed according to three scenarios: i) with no specific public policy in favour of sustainable development, ii) with an aggressive public policy against energy wasting in homes, iii) integrating the tendency of inhabitants to innovate and their level of sensitivity to levers set out by public policies. The cells' potentials are mapped. Some of them are selected for carrying out simulations of plausible transformations of the built fabric, and visualising in 3D the changed aspects of façades in certain streets

La capacité d’adaptation : clé d’entrée dans le système complexe de la gestion imbriquée eau-territoire. : L’Île de Camargue et le Plan du Bourg dans le delta du Rhône

S’inscrivant dans le contexte de la directive cadre sur l’eau, le programme de recherche « Eaux et Territoires » ambitionne de mettre en relation les connaissances relatives au fonctionnement des hydrosystèmes et celles portant sur les territoires, afin d’éclairer les politiques actuelles et à venir. Dans ce cadre, le ministère du Développement durable, le CNRS et IRSTEA ont soutenu dix-neuf projets de recherche, de 2007 à 2015, couvrant une grande diversité de thématiques allant de la gestion technique des risques et opportunités liés à l’eau aux questions de gouvernance et de relations entre acteurs. Cet ouvrage recueille à la fois des synthèses et résultats de ces recherches, propose le regard d’acteurs opérationnels sur les sujets abordés et des fiches thématiques sur des questions d’actualité. Il s’adresse à des chercheurs et à des étudiants, comme aux acteurs de l’eau et des territoires

Can effects of social intentions on reach-to-grasp kinematics be modified by how I feel ?

International audienceOBJECTIVE : A novel line of research suggest that social context shapes action kinematics (Becchio et al., 2010 ; Quesque et al., 2015), evidencing that in the context of a social interaction, flexible online adjustments take place between partners. Here, we investigated to what extent these kinematics adjustement could be mediated by affective component. Affective sounds were used to induce emotionnal states in adult participants who was performing a reach-to-grasp task. PARTICIPANTS & METHODSMovements of ten participants were recorded using 6 Oqus infrared cameras (Qualisys). The participants were instructed to reach toward and grasp a dowel, and then place it as fast as possible to a hand which was either the real hand of the partner (Social condition) or a plastered replica of a hand. In this last condition, the partner could be present but in far space (Passive Observer condition) or not present at all (Individual condition). During each condition, a sound known to induce positive, neutral or negative emotions was broadcasted. In addition to motion capture, psychopathological and emotional scales were used.RESULTSStatistical analyses replicated previous results increased duration of movement when performed in the social context. However, no significant interaction effect between kinematics measures and emotional conditions could be found; probably explained by an inappropriate emotional induction as shown by the results in the emotional scales.CONCLUSIONOur results replicated previous findings underlying the importance of taking into account the influence of social context in current models of motor control. However, it is still unclear whether affectives states of the agent might also modulate sensorimotor processes and how it would interact with the influence of the social context

Can effects of social intentions on reach-to-grasp kinematics be modified by how I feel ?

International audienceOBJECTIVE : A novel line of research suggest that social context shapes action kinematics (Becchio et al., 2010 ; Quesque et al., 2015), evidencing that in the context of a social interaction, flexible online adjustments take place between partners. Here, we investigated to what extent these kinematics adjustement could be mediated by affective component. Affective sounds were used to induce emotionnal states in adult participants who was performing a reach-to-grasp task. PARTICIPANTS & METHODSMovements of ten participants were recorded using 6 Oqus infrared cameras (Qualisys). The participants were instructed to reach toward and grasp a dowel, and then place it as fast as possible to a hand which was either the real hand of the partner (Social condition) or a plastered replica of a hand. In this last condition, the partner could be present but in far space (Passive Observer condition) or not present at all (Individual condition). During each condition, a sound known to induce positive, neutral or negative emotions was broadcasted. In addition to motion capture, psychopathological and emotional scales were used.RESULTSStatistical analyses replicated previous results increased duration of movement when performed in the social context. However, no significant interaction effect between kinematics measures and emotional conditions could be found; probably explained by an inappropriate emotional induction as shown by the results in the emotional scales.CONCLUSIONOur results replicated previous findings underlying the importance of taking into account the influence of social context in current models of motor control. However, it is still unclear whether affectives states of the agent might also modulate sensorimotor processes and how it would interact with the influence of the social context