Matching Local Invariant Features: How Can Contextual Information Help?

International audienceLocal invariant features are a powerful tool for finding correspondences between images since they are robust to cluttered background, occlusion and viewpoint changes. However, they suffer the lack of global information and fail to resolve ambiguities that can occur when an image has multiple similar regions. Considering some global information will clearly help to achieve better performances. The question is which information to use and how to use it. While previous approaches use context for description, this paper shows that better results are obtained if contextual information is included in the matching process. We compare two different methods which use context for matching and experiments show that a relaxation based approach gives better results

Matching Local Invariant Features with Contextual Information : an Experimental Evaluation

The main advantage of using local invariant features is their local character which yields robustness to occlusion and varying background. Therefore, local features have proved to be a powerful tool for finding correspondences between images, and have been employed in many applications. However, the local character limits the descriptive capability of features descriptors, and local features fail to resolve ambiguities that can occur when an image shows multiple similar regions. Considering some global information will clearly help to achieve better performances. The question is which information to use and how to use it. Context can be used to enrich the description of the features, or used in the matching step to filter out mismatches. In this paper, we compare different recent methods which use context for matching and show that better results are obtained if contextual information is used during the matching process. We evaluate the methods in two applications: wide baseline matching and object recognition, and it appears that a relaxation based approach gives the best results

Subontology Extraction Using Hyponym and Hypernym Closure on is-a Directed Acyclic Graphs

International audienceOntologies are successfully used as semantic guides when navigating through the huge and ever increasing quantity of digital documents. Nevertheless, the size of numerous domain ontologies tends to grow beyond the human capacity to grasp information. This growth is problematic for a lot of key applications that require user interactions such as document annotation or ontology modification/evolution. The problem could be partially overcome by providing users with a sub-ontology focused on their current concepts of interest. A sub-ontology restricted to this sole set of concepts is of limited interest since their relationships can generally not be explicit without adding some of their hyponyms and hypernyms. This paper proposes efficient algorithms to identify these additional key concepts based on the closure of two common graph operators: the least common-ancestor and greatest common descendant. The resulting method produces ontology excerpts focused on a set of concepts of interest and is fast enough to be used in interactive environments. As an example, we use the resulting program, called OntoFocus (http://www.ontotoolkit.mines-ales.fr/), to restrict, in few seconds, the large Gene Ontology (~30,000 concepts) to a sub-ontology focused on concepts annotating a gene related to breast cancer

A simple and efficient eye detection method in color images

International audienceIn this paper we propose a simple and efficient eye detection method for face detection tasks in color images. The algorithm first detects face regions in the image using a skin color model in the normalized RGB color space. Then, eye candidates are extracted within these regions. Finally, using the anthrophological characteristics of human eyes, the pairs of eye regions are selected. The proposed method is simple and fast, since it needs no template matching step for face verification. It is robust because it can deals with face rotation. Experimental results show the validity of our approach, a correct eye detection rate of 98.4% is achieved using a subset of the AR face database

Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees

Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these methods have been extended in order to deal with uncertainty in the output variable, using for example a quantile loss in Random Forests (Meinshausen, 2006). To the best of our knowledge, no extension has been provided yet for dealing with uncertainties in the input variables, even though such uncertainties are common in practical situations. We propose here such an extension by showing how standard regression trees optimizing a quadratic loss can be adapted and learned while taking into account the uncertainties in the inputs. By doing so, one no longer assumes that an observation lies into a single region of the regression tree, but rather that it belongs to each region with a certain probability. Experiments conducted on several data sets illustrate the good behavior of the proposed extension.Comment: 9 page

From Theoretical Framework To Generic Semantic Measures Library

International audienceThanks to the ever-increasing use of the Semantic Web, a growing number of entities (e.g. documents) are characterized by non-ambiguous mean-ings. Based on this characterization, entities can subsequently be compared us-ing semantic measures. A plethora of measures have been designed given their critical importance in numerous treatments relying on ontologies. However, the improvement and use of semantic measures are currently hampered by the lack of a dedicated theoretical framework and an extensive generic software solution dedicated to them. To meet these needs, this paper presents a unified theoretical framework of graph-based semantic measures, from which we developed the open source Semantic Measures Library and toolkit; a solution that paves the way for straightforward design, computation and analysis of semantic measures for both users and developers. Downloads, documentation and technical support at dedicated website http://www.semantic-measures-library.org

Sélection Robuste de Mesures de Similarité Sémantique à partir de Données Incertaines d'Expertise

National audienceKnowledge-based semantic measures are cornerstone to exploit ontologies not only for exact inferences or retrieval processes, but also for data analyses and inexact searches. Abstract theoretical frameworks have recently been proposed in order to study the large diversity of measures available; they demonstrate that groups of measures are particular instantiations of general parameterized functions. In this paper, we study how such frameworks can be used to support the selection/design of measures. Based on (i) a theoretical framework unifying the measures, (ii) a software solution implementing this framework and (iii) a domain-specific benchmark, we define a semi-supervised learning technique to distinguish best measures for a concrete application. Next, considering uncertainty in both experts’ judgments and measures’ selection process, we extend this proposal for robust selection of semantic measures that best resists to these uncertainties. We illustrate our approach through a real use case in the biomedical domain..L'exploitation d'ontologies pour la recherche d'information, la découverte de connaissances ou le raisonnement approché nécessite l'utilisation de mesures sémantiques qui permettent d'estimer le degré de similarité entre des entités lexicales ou conceptuelles. Récemment un cadre théorique abstrait a été proposé afin d'unifier la grande diversité de ces mesures, au travers de fonctions paramétriques générales. Cet article propose une utilisation de ce cadre unificateur pour choisir une mesure. A partir du (i) cadre unificateur exprimant les mesures basées sur un ensemble limité de primitives, (ii) logiciel implémentant ce cadre et (iii) benchmark d'un domaine spécifique, nous utilisons une technique d'apprentissage semi-supervisé afin de fournir la meilleure mesure sémantique pour une application donnée. Ensuite, sachant que les données fournies par les experts sont entachées d'incertitude, nous étendons notre approche pour choisir la plus robuste parmi les meilleures mesures, i.e. la moins perturbée par les erreurs d'évaluation experte. Nous illustrons notre approche par une application dans le domaine biomédical. Mots-clés: Cadre unificateur, robustesse de mesures, incertitude d'expert, mesures de similarité sémantique, ontologies

Concurrent Changes of Brain Functional Connectivity and Motor Variability When Adapting to Task Constraints

In behavioral neuroscience, the adaptability of humans facing different constraints has been addressed on one side at the brain level, where a variety of functional networks dynamically support the same performance, and on the other side at the behavioral level, where fractal properties in sensorimotor variables have been considered as a hallmark of adaptability. To bridge the gap between the two levels of observation, we have jointly investigated the changes of network connectivity in the sensorimotor cortex assessed by modularity analysis and the properties of motor variability assessed by multifractal analysis during a prolonged tapping task. Four groups of participants had to produce the same tapping performance while being deprived from 0, 1, 2, or 3 sensory feedbacks simultaneously (auditory and/or visual and/or tactile). Whereas tapping performance was not statistically different across groups, the number of brain networks involved and the degree of multifractality of the inter-tap interval series were significantly correlated, increasing as a function of feedback deprivation. Our findings provide first evidence that concomitant changes in brain modularity and multifractal properties characterize adaptations underlying unchanged performance. We discuss implications of our findings with respect to the degeneracy properties of complex systems, and the entanglement of adaptability and effective adaptation