Extraction et analyse de l'impact émotionnel des images

Session "Articles"National audienceCet article propose une méthode d'extraction de l'impact émotionnel des images à partir de descripteurs récents. Très souvent, on associe les émotions à l'expression du visage, mais nous avons décidé de ne pas faire de cette information la principale information émotionnelle des images naturelles, qui en général ne contiennent pas de visages. Nous avons ainsi effectué nos tests sur une base diversifiée, construite à partir d'images à faible contenu sémantique. La complexité des émotions a été prise en compte en intégrant, au processus de classification, les résultats de tests psycho-visuels que nous avons mis en place. Vingt cinq observateurs ont participé aux tests. Ils ont évalué la nature et la puissance des émotions ressenties. Nous avons choisi un réseau de neurones multicouches pour la classification. Le taux de réussite moyen obtenu lors de la classification est de 56,15% ; ce qui est encourageant au regard des résultats de la littérature

Extraction of emotional impact in colour images

International audienceThis paper proposes a method to extract the emotional impact of images. Emotions are often associated with facial expressions, but we decided consider other features as first emotional characteristic of natural images, which, in general, does not contain faces. For a seek of generally we have built a new image database composed of a large variety of low semantic images. We used colour images because often colours and emotions are supposed to be linked. For the modelling of the emotions, we considered colours features completed with other recent and efficient descriptors. We supposed that different features used could also implicitly encode high-level information about emotions. The concept of emotion is not easy to model. The perception of emotion is not only influenced by the content and the colour of the images. It is also modified by some personal experiences like cultural aspects and personal semantic associated to some colours or objects. The complexity of emotion modelling was considered in classification process through psycho-visual tests. The twenty-five observers assessed the nature and the power of emotions they felt. These tests allowed us to distinguish three classes of emotions, which are "Negative", "Neutral" and "Positive" emotions. We used a Support Vector Machine for classification and the average success rate is 51,75%; that is really relevant regarding the equivalent results in the literature

Gender influences on subjective evaluations in images

International audienceThis paper proposes to study gender influences on subjective evaluations in images. Our goal is to verify if some common conclusions in psychology experiences are confirmed during the subjective evaluations we organized. Our database and our test strategy are the main originalities of this work. We built a new low semantic images database, composed of 350 natural images. The tests were accessible via the Internet and each participant rated 24 randomly selected images. 1741 participants, including 848 men (48.71%) and 893 women (51.29%) assessed our 350 images according to the nature and the power of the emotion. We also ask them to quick evaluate each image (under10 seconds) to have really their "primary" emotions. During the analysis of the results of the tests, we observed that women tend to associate more often positive or negative emotions to images than men who consider those images as neutral. The additional neutral ones scored by men are generally classified positive or negative by women. In fact, women scored positive with the low power some images men scored neutral. These results confirm potential differences in gender emotion evaluations and also the common conclusion that women express more emotions than men

Extraction de l'impact émotionnel des images

International audienceCet article propose une méthode d'extraction de l'impact émotionnel des images à partir de descripteurs bas niveau. Nous avons émis l'hypothèse que la précision de ces derniers encoderait des informations haut niveau intéressantes voire discriminantes pour les émotions. Il n'existe à ce jour aucun descripteur particulièrement adapté à l'étude de l'impact émotionnel des images. Les émotions ressenties dépendent, en effet, de plusieurs informations dans l'image mais également de sa nature (très sémantique ou non) ou encore de la durée d'observation. Plus ce temps est long plus l'interprétation sémantique de l'image prend le dessus sur l'émotion " primaire ". À ces descripteurs nous avons associé deux classifieurs performants, particulièrement adaptés à des discriminations d'informations complexes. Il s'agit d'un réseau de neurones multicouche et d'un SVM dans son extension multiclasse basée sur la stratégie " un contre un ". Très souvent, on associe les émotions à l'expression du visage, mais nous avons décidé de ne pas faire de cette information la principale caractéristique émotionnelle des images naturelles, qui en général ne contiennent pas de visages. Nous avons, à cet effet, effectué nos tests sur une base diversifiée de 350 images, construite à partir d'images à faible contenu sémantique. Notre choix de descripteurs est basé sur des supposés liens entre les émotions et le contenu des images mais également sur la précision qu'offrent certains descripteurs de traitement d'images en indexation ou en catégorisation. La complexité des émotions a été prise en compte en intégrant, au processus de classification, les résultats de tests psychovisuels que nous avons mis en place. Nous avons défini trois classes d'émotions. Les taux de réussite moyens obtenus lors de la classification sont de 56,15 % pour le réseau de neurones et 55,25 % pour le SVM. Ces résultats sont encourageants au regard des résultats de la littérature. Ces tests confirment aussi l'hypothèse que les descripteurs choisis sont complémentaires dans notre processus d'extraction des émotions

Statistical region-based active contours for segmentation: an overview

International audienceIn this paper we propose a brief survey on geometric variational approaches and more precisely on statistical region-based active contours for medical image segmentation. In these approaches, image features are considered as random variables whose distribution may be either parametric, and belongs to the exponential family, or non-parametric estimated with a kernel density method. Statistical region-based terms are listed and reviewed showing that these terms can depict a wide spectrum of segmentation problems. A shape prior can also be incorporated to the previous statistical terms. A discussion of some optimization schemes available to solve the variational problem is also provided. Examples on real medical images are given to illustrate some of the given criteria

Statistical region-based active contours with exponential family observations

International audienceIn this paper, we focus on statistical region-based active contour models where image features (e.g. intensity) are random variables whose distribution belongs to some parametric family (e.g. exponential) rather than confining ourselves to the special Gaussian case. Using shape derivation tools, our effort focuses on constructing a general expression for the derivative of the energy (with respect to a domain) and derive the corresponding evolution speed. A general result is stated within the framework of multi-parameter exponential family. More particularly, when using Maximum Likelihood estimators, the evolution speed has a closed-form expression that depends simply on the probability density function, while complicating additive terms appear when using other estimators, e.g. momentsmethod. Experimental results on both synthesized and real images demonstrate the applicability of our approach

Sediment pollution impacts sensory ability and performance of settling coral-reef fish

© 2015, Springer-Verlag Berlin Heidelberg. Marine organisms are under threat globally from a suite of anthropogenic sources, but the current emphasis on global climate change has deflected the focus from local impacts. While the effect of increased sedimentation on the settlement of coral species is well studied, little is known about the impact on larval fish. Here, the effect of a laterite “red soil” sediment pollutant on settlement behaviour and post-settlement performance of reef fish was tested. In aquarium tests that isolated sensory cues, we found significant olfaction-based avoidance behaviour and disruption of visual cue use in settlement-stage larval fish at 50 mg L−1, a concentration regularly exceeded in situ during rain events. In situ light trap catches showed lower abundance and species richness in the presence of red soil, but were not significantly different due to high variance in the data. Prolonged exposure to red soil produced altered olfactory cue responses, whereby fish in red soil made a likely maladaptive choice for dead coral compared to controls where fish chose live coral. Other significant effects of prolonged exposure included decreased feeding rates and body condition. These effects on fish larvae reared over 5 days occurred in the presence of a minor drop in pH and may be due to the chemical influence of the sediment. Our results show that sediment pollution of coral reefs may have more complex effects on the ability of larval fish to successfully locate suitable habitat than previously thought, as well as impacting on their post-settlement performance and, ultimately, recruitment success

Co2Vis: A Visual Analytics Tool for Mining Co-Expressed and Co-Regulated Genes Implied in HIV Infections

International audienceOne of the key challenges in human health is the identification of disease-causing genes like AIDS (Acquired ImmunoDeficiency Syndrome). Numerous studies have addressed this challenge through gene expression analysis. Due to the amount of data available, processing DNA microarrays in a way that makes biomedical sense is still a major issue.Statistical methods and data mining techniques play a key role in discovering previously unknown knowledge. However, applying such techniques in this context is difficult because the number of measurement points (i.e., gene expression levels) is much higher than the number of samples resulting in the well-known curse of dimensionality problem also called the high feature-to-sample ratio.We propose a combination of data mining and visual analytics methods to identify and render groups of genes implied in HIV infections and sharing common behaviors

MicroRNA-155 is induced during the macrophage inflammatory response

The mammalian inflammatory response to infection involves the induction of several hundred genes, a process that must be carefully regulated to achieve pathogen clearance and prevent the consequences of unregulated expression, such as cancer. Recently, microRNAs (miRNAs) have emerged as a class of gene expression regulators that has also been linked to cancer. However, the relationship between inflammation, innate immunity, and miRNA expression is just beginning to be explored. In the present study, we use microarray technology to identify miRNAs induced in primary murine macrophages after exposure to polyriboinosinic:polyribocytidylic acid or the cytokine IFN-{beta}. miR-155 was the only miRNA of those tested that was substantially up-regulated by both stimuli. It also was induced by several Toll-like receptor ligands through myeloid differentiation factor 88- or TRIF-dependent pathways, whereas up-regulation by IFNs was shown to involve TNF-{alpha} autocrine signaling. Pharmacological inhibition of the kinase JNK blocked induction of miR-155 in response to either polyriboinosinic:polyribocytidylic acid or TNF-{alpha}, suggesting that miR-155-inducing signals use the JNK pathway. Together, these findings characterize miR-155 as a common target of a broad range of inflammatory mediators. Importantly, because miR-155 is known to function as an oncogene, these observations identify a potential link between inflammation and cancer

OrderGeneMiner : Logiciel pour l'extraction et la visualisation de motifs partiellement ordonnés à partir de puces à ADN

Démonstration du logiciel @ 13e Conférence Francophone sur l'Extraction et la Gestion des Connaissances (EGC 2013)Le Virus de l'Immunodéficience Humaine (VIH) est actuellement un problème majeur de santé publique. Depuis l'identification du VIH, plus de 20 millions de personnes ont été identifiées. Le VIH continue de ravager les populations dans le monde entier avec 3 millions de nouvelles infections par an. Contrairement au cancer, les approches de biologie intégrative sont toujours rares dans le domaine de la lutte contre le HIV. Dans cet article, nous proposons de contribuer au développement d'une telle stratégie, en présentant un logiciel de fouille de données qui va permettre d'appliquer les concepts de motifs séquentiels et de motifs partiellement ordonnés aux données de puces à ADN. Ce logiciel se focalise sur les besoins des biologistes: 1) permet à l'expert d'intéragir dans le processus d'extraction des motifs; 2) offre une visualisation des motifs extrait sous la forme d'un graphe coloré qui résume un ensemble de motifs séquentiels. Il en résulte une visualisation plus compacte et simple qui facilite l'interprétation des experts