Intuitivno pretraživanje baze slike kao potpora označavanju slika

Image annotation is typically performed manually since automatic image annotation approaches have not matured yet to be used in practice. Consequently, image annotation is a labour intensive and time consuming task. In this paper, we show how an image browsing system can be employed to support efficient and effective (manual) annotation of image databases. In contrast to other approaches, which typically present images in a linear fashion, we employ a visualisation where images are arranged by mutual visual similarity. Since in this arrangement similar images are close to each other, they can easily be selected and annotated together. Organisation on a grid layout prevents image overlap and thus contributes to a clear presentation. Large image databases are handled through a hierarchical data structure where each image in the visualisation can correspond to a cluster of images that can be expanded by the user. Experimental results indicate that annotation can be performed faster on our proposed system.Označavanje slika obično se obavlja ručno jer automatski pristupi još nisu dovoljno kvalitetni kako bi se koristili u praksi. Zbog toga je označavanje slika u bazi vremenski zahtjevno. U ovom radu pokazat ćemo kako se sustav za pregled slika u bazi može koristiti kao učinkovita potpora ručnom označavanju slika. Za razliku od drugih pristupa, koji prikazuju slike u linearnom poretku, korištena je vizualizacija u kojoj su slike složene po međusobnoj sličnosti. Budući da su na taj način slične slike međusobno blizu jedna drugoj, lako ih je selektirati i zajednički označiti. Slike su organizirane u mrežni prikaz radi sprječavanja preklapanja i jasnije prezentacije. Velike baze podataka organizirane su u hijerarhijsku strukturu gdje svaka slika u pojedinoj vizualizaciji može pripadati skupu slika čiji prikaz korisnik po želji može proširivati. Rezultati provedenih eksperimenata pokazuju da se označavanje slika pomoću predloženog sustava može obavljati brže nego na uobičajeni način

Consensus ou fusion de segmentation pour quelques applications de détection ou de classification en imagerie

Récemment, des vraies mesures de distances, au sens d’un certain critère (et possédant de bonnes propriétés asymptotiques) ont été introduites entre des résultats de partitionnement (clustering) de donnés, quelquefois indexées spatialement comme le sont les images segmentées. À partir de ces métriques, le principe de segmentation moyenne (ou consensus) a été proposée en traitement d’images, comme étant la solution d’un problème d’optimisation et une façon simple et efficace d’améliorer le résultat final de segmentation ou de classification obtenues en moyennant (ou fusionnant) différentes segmentations de la même scène estimée grossièrement à partir de plusieurs algorithmes de segmentation simples (ou identiques mais utilisant différents paramètres internes). Ce principe qui peut se concevoir comme un débruitage de données d’abstraction élevée, s’est avéré récemment une alternative efficace et très parallélisable, comparativement aux méthodes utilisant des modèles de segmentation toujours plus complexes et plus coûteux en temps de calcul. Le principe de distance entre segmentations et de moyennage ou fusion de segmentations peut être exploité, directement ou facilement adapté, par tous les algorithmes ou les méthodes utilisées en imagerie numérique où les données peuvent en fait se substituer à des images segmentées. Cette thèse a pour but de démontrer cette assertion et de présenter différentes applications originales dans des domaines comme la visualisation et l’indexation dans les grandes bases d’images au sens du contenu segmenté de chaque image, et non plus au sens habituel de la couleur et de la texture, le traitement d’images pour améliorer sensiblement et facilement la performance des méthodes de détection du mouvement dans une séquence d’images ou finalement en analyse et classification d’images médicales avec une application permettant la détection automatique et la quantification de la maladie d’Alzheimer à partir d’images par résonance magnétique du cerveau.Recently, some true metrics in a criterion sense (with good asymptotic properties) were introduced between data partitions (or clusterings) even for data spatially ordered such as image segmentations. From these metrics, the notion of average clustering (or consensus segmentation) was then proposed in image processing as the solution of an optimization problem and a simple and effective way to improve the final result of segmentation or classification obtained by averaging (or fusing) different segmentations of the same scene which are roughly estimated from several simple segmentation models (or obtained with the same model but with different internal parameters). This principle, which can be conceived as a denoising of high abstraction data, has recently proved to be an effective and very parallelizable alternative, compared to methods using ever more complex and time-consuming segmentation models. The principle of distance between segmentations, and averaging of segmentations, in a criterion sense, can be exploited, directly or easily adapted, by all the algorithms or methods used in digital imaging where data can in fact be substituted to segmented images. This thesis proposal aims at demonstrating this assertion and to present different original applications in various fields in digital imagery such as the visualization and the indexation in the image databases, in the sense of the segmented contents of each image, and no longer in the common color and texture sense, or in image processing in order to sensibly and easily improve the detection of movement in the image sequence or finally in analysis and classification in medical imaging with an application allowing the automatic detection and quantification of Alzheimer’s disease

Adaptively Browsing Image Databases with PIBE

Browsing large image collections is a complex and often tedious task, due to the semantic gap existing between the user subjective notion of similarity and the one according to which a browsing system organizes the images. In this paper we propose PIBE, an adaptive image browsing system, which provides users with a hierarchical view of images (the Browsing Tree) that can be customized according to user preferences. A key feature of PIBE is that it maintains local similarity criteria for each portion of the Browsing Tree. This makes it possible both to avoid costly global reorganization upon execution of user actions and, combined with a persistent storage of the Browsing Tree, to efficiently support multiple browsing tasks. We present the basic principles of PIBE and report experimental results showing the effectiveness of its browsing and personalization functionalities