Automatic blur detection for meta-data extraction in content-based retrieval context

International audienceDuring the last few years, image by content retrieval is the aim of many studies. A lot of systems were introduced in order to achieve image indexation. One of the most common method is to compute a segmentation and to extract different parameters from regions. However, this segmentation step is based on low level knowledge, without taking into account simple perceptual aspects of images, like the blur. When a photographer decides to focus only on some objects in a scene, he certainly considers very differently these objects from the rest of the scene. It does not represent the same amount of information. The blurry regions may generally be considered as the context and not as the information container by image retrieval tools. Our idea is then to focus the comparison between images by restricting our study only on the non blurry regions, using then these meta data. Our aim is to introduce different features and a machine learning approach in order to reach blur identification in scene images

Blur identification in image processing

International audienceThe aim of this study is to achieve a blur identification task in still images. In fact, in photographic camera, the optical lenses may be set in a way to clearly distinct two areas in the image : the blurry one and the non blurry one. An automatic segmentation coupled to specific descriptors allow first to describe any region of the image. Then, a supervised learning processes permits to build a classifier able to decide for each unknown region the label “Blurry” or “Sharp”. We discuss here precisely the overall process, from the objective choice of the segmentation algorithm to the presentation of the different introduced descriptors. Finally, some results are presented validating such an approach

étude comparative de méthodes de segmentation dans une approche orientée indexation.

National audienceCet article présente une étude objective et quantitative des méthodes de segmentation pour la recherche d'images par le contenu. Une méthode pyramidale de segmentation couleur par propagation d'étiquettes est introduite avec différentes possibilités d'initialisation des germes. Une nouvelle approche d'évaluation dans un contexte orienté indexation est proposée. Des descripteurs sont définis pour valider la performance de stabilité. Des résultats sur une banque d'images test conséquente sont présentés

About Segmentation Step in Content-based Image Retrieval Systems

International audienceDespite of the hope arised a few years ago, Content Based Image Retrieval - CBIR - systems has not reached the initial goal, ie to manage and search images in database: we are unable to link the semantic sens of an image to numerical values. However, some members of the community have begun the necessary introspection. The analyze of each step of the feature extraction will allow us to overcome actual problematics and to take the right path in the future. In this context, we propose in this paper to discuss about a low-level tool frequently used: the segmentation step. In the general context of scene images, we evaluate the stability of some classical algorithms using a basic protocol. The quite inefficiency of all approaches let us conclude to the necessity to use meta-data and any other collected informations during this first segmentation step

Estudo de trajetorias de desenvolvimento local e da construcao do espaco rural no Nordeste semi-arido.

O estudo das trajetórias de desenvolvimento é um instrumento elaborado para poder explicitar e representar as transformações dos sistemas de produção e das formas de organização dos atores na escala local. O exemplo da pequena região de Massaroca (Juazeiro-Bahia) apresenta uma ilustração desta metodologia que valoriza a história agrária, a abordagem espacial e apóia-se em entrevistas de agricultores e técnicos locais. Resultados dos estudos de trajetórias de desenvolvimento efetuados em várias local idades do Nordeste Semi-àrido são objeto de uma análise comparativa. Ela é realizada por meio: (I) da interpretação da diversidade ou da semelhança das evoluções em locais distintos para épocas dadas e, (2) da interpretação de evoluções parecidas em locais e momentos diferentes. A comparação evidencia mecanismos diferenciados de evolução das dinâmicas rurais. Ela contribui para o entendimento e a representação das condições e dos processos de diferenciação das situações agrárias. A caracterização das situações estudadas permite a identificação de "tipos de espaço" diferenciados. A cada "tipo" correspondem formas de organização local específicas, estratégias e práticas semelhantes. A análise dos mecanismos de transição entre estas situações leva à identificação de um modelo de evolução das pequenas regiões. Cadeias de evolução dos espaços locais são assim evidenciadas. Finalmente, os autores tratam da vai idade do modelo aplicado ao caso da agricultura familiar do Nordeste Semi-Árido com a perspectiva de identificar as possibilidades de ação de apoio ao desenvolvimento rural

Composition and Diversity of Ant Species into Leaf Litter of Two Fragments of a Semi-Deciduous Seasonal Forest in the Atlantic Forest Biome in Barra do Choça, Bahia, Brazil

We present here the preliminary results of a study of leaf litter ant diversity in remnant areas of semi-deciduous seasonal forests in the Atlantic Forest biome. Standardized collections were made in 2011, using pitfall and Winkler traps in two fragments of native forest belonging to the municipality of Barra do Choça in the micro-region of the Planalto da Conquista, in Southwestern Bahia State, Brazil; 107 species from 37 ant genera and 9 subfamilies were collected. The observed richness was high, and the diversity indices (Shannon-Wiener) of the two fragments suggest that in spite of being strongly impacted by anthropogenic actions, they maintained a high faunal diversity levels, similar to those observed in other original Atlantic Forest sites in Bahia State. Analyses of the accumulated species richness curves and estimated richnesses (Jackknife 2), however, demonstrated that the survey efforts expended were not sufficient to capture all of the species present. The high observed numbers of unique species, smooth curves of the accumulated richness graphs, and high values of estimated richness suggested that the survey areas were quite heterogeneous. These results furnished new information concerning regional biodiversity that will be useful as initial references for continuing studies of fragmentation processes in the region

Distributed Graph Clustering using Modularity and Map Equation

We study large-scale, distributed graph clustering. Given an undirected graph, our objective is to partition the nodes into disjoint sets called clusters. A cluster should contain many internal edges while being sparsely connected to other clusters. In the context of a social network, a cluster could be a group of friends. Modularity and map equation are established formalizations of this internally-dense-externally-sparse principle. We present two versions of a simple distributed algorithm to optimize both measures. They are based on Thrill, a distributed big data processing framework that implements an extended MapReduce model. The algorithms for the two measures, DSLM-Mod and DSLM-Map, differ only slightly. Adapting them for similar quality measures is straight-forward. We conduct an extensive experimental study on real-world graphs and on synthetic benchmark graphs with up to 68 billion edges. Our algorithms are fast while detecting clusterings similar to those detected by other sequential, parallel and distributed clustering algorithms. Compared to the distributed GossipMap algorithm, DSLM-Map needs less memory, is up to an order of magnitude faster and achieves better quality.Comment: 14 pages, 3 figures; v3: Camera ready for Euro-Par 2018, more details, more results; v2: extended experiments to include comparison with competing algorithms, shortened for submission to Euro-Par 201