Graph edit distance or graph edit pseudo-distance?

Graph Edit Distance has been intensively used since its appearance in 1983. This distance is very appropriate if we want to compare a pair of attributed graphs from any domain and obtain not only a distance, but also the best correspondence between nodes of the involved graphs. In this paper, we want to analyse if the Graph Edit Distance can be really considered a distance or a pseudo-distance, since some restrictions of the distance function are not fulfilled. Distinguishing between both cases is important because the use of a distance is a restriction in some methods to return exact instead of approximate results. This occurs, for instance, in some graph retrieval techniques. Experimental validation shows that in most of the cases, it is not appropriate to denominate the Graph Edit Distance as a distance, but a pseudo-distance instead, since the triangle inequality is not fulfilled. Therefore, in these cases, the graph retrieval techniques not always return the optimal graph

Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation

Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various datasets of labeled graphs. However, the design of effective learning procedures operating in the space of labeled graphs is still a challenging problem, especially from the computational complexity viewpoint. In this paper, we present a major improvement of a general-purpose classifier for graphs, which is conceived on an interplay between dissimilarity representation, clustering, information-theoretic techniques, and evolutionary optimization algorithms. The improvement focuses on a specific key subroutine devised to compress the input data. We prove different theorems which are fundamental to the setting of the parameters controlling such a compression operation. We demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well-known datasets of labeled graphs, considering as distinct performance indicators the classification accuracy, computing time, and parsimony in terms of structural complexity of the synthesized classification models. The results show state-of-the-art standards in terms of test set accuracy and a considerable speed-up for what concerns the computing time.Comment: Revised versio

Correspondence consensus of two sets of correspondences through optimisation functions.

We present a consensus method which, given the two correspondences between sets of elements generated by separate entities, enounces a final correspondence consensus considering the existence of outliers. Our method is based on an optimisation technique that minimises the cost of the correspondence while forcing (to the most) to be the mean correspondence of the two original correspondences. The method decides the mapping of the elements that the original correspondences disagree and returns the same element mapping when both correspondences agree. We first show the validity of the method through an experiment in ideal conditions based on palmprint identification, and subsequently present two practical experiments based on image retrieval

Obtaining the consensus of multiple correspondences between graphs through online learning.

In structural pattern recognition, it is usual to compare a pair of objects through the generation of a correspondence between the elements of each of their local parts. To do so, one of the most natural ways to represent these objects is through attributed graphs. Several existing graph extraction methods could be implemented and thus, numerous graphs, which may not only differ in their nodes and edge structure but also in their attribute domains, could be created from the same object. Afterwards, a matching process is implemented to generate the correspondence between two attributed graphs, and depending on the selected graph matching method, a unique correspondence is generated from a given pair of attributed graphs. The combination of these factors leads to the possibility of a large quantity of correspondences between the two original objects. This paper presents a method that tackles this problem by considering multiple correspondences to conform a single one called a consensus correspondence, eliminating both the incongruences introduced by the graph extraction and the graph matching processes. Additionally, through the application of an online learning algorithm, it is possible to deduce some weights that influence on the generation of the consensus correspondence. This means that the algorithm automatically learns the quality of both the attribute domain and the correspondence for every initial correspondence proposal to be considered in the consensus, and defines a set of weights based on this quality. It is shown that the method automatically tends to assign larger values to high quality initial proposals, and therefore is capable to deduce better consensus correspondences

Electronic Imaging & the Visual Arts. EVA 2019 Florence

The Publication is following the yearly Editions of EVA FLORENCE. The State of Art is presented regarding the Application of Technologies (in particular of digital type) to Cultural Heritage. The more recent results of the Researches in the considered Area are presented. Information Technologies of interest for Culture Heritage are presented: multimedia systems, data-bases, data protection, access to digital content, Virtual Galleries. Particular reference is reserved to digital images (Electronic Imaging & the Visual Arts), regarding Cultural Institutions (Museums, Libraries, Palace - Monuments, Archaeological Sites). The International Conference includes the following Sessions: Strategic Issues; New Science and Culture Developments & Applications; New Technical Developments & Applications; Cultural Activities – Real and Virtual Galleries and Related Initiatives, Access to the Culture Information. One Workshop regards Innovation and Enterprise. The more recent results of the Researches at national and international level are reported in the Area of Technologies and Culture Heritage, also with experimental demonstrations of developed Activities

E-health and e-welfare of Finland - Check point 2015

The new e-health and e-welfare strategy in Finland aims to support the renewal of the social welfare and health care services and the active role of citizens in maintaining their own well-being. The means include the development of knowledge management and increasing the provision of online services. The overall structural changes taking place in Finnish health and social care will also influence information and communication technologies (ICT). The report provides information about the change in the services and the service system brought on by ICT over time. The report illustrates the status in 2014 as compared with the strategic outcomes and objectives set on ICT to support performance and renewal of social welfare and health care. The results are condensed from four surveys for a comprehensive view: availability and use of ICT in health care as well as in social care, usability of the systems for physicians, and citizens´ use and anticipations. These are accompanied by a review of Finnish health care system and ICT development. For the international reader, the report provides an overview of progressive nationwide activities towards better e-services in Finland

Learning the Consensus of Multiple Correspondences between Data Structures

En aquesta tesi presentem un marc de treball per aprendre el consens donades múltiples correspondències. S'assumeix que les diferents parts involucrades han generat aquestes correspondències per separat, i el nostre sistema actua com un mecanisme que calibra diferents característiques i considera diferents paràmetres per aprendre les millors assignacions i així, conformar una correspondència amb la major precisió possible a costa d'un cost computacional raonable. Aquest marc de treball de consens és presentat en una forma gradual, començant pels desenvolupaments més bàsics que utilitzaven exclusivament conceptes ben definits o únicament un parell de correspondències, fins al model final que és capaç de considerar múltiples correspondències, amb la capacitat d'aprendre automàticament alguns paràmetres de ponderació. Cada pas d'aquest marc de treball és avaluat fent servir bases de dades de naturalesa variada per demostrar efectivament que és possible tractar diferents escenaris de matching. Addicionalment, dos avanços suplementaris relacionats amb correspondències es presenten en aquest treball. En primer lloc, una nova mètrica de distància per correspondències s'ha desenvolupat, la qual va derivar en una nova estratègia per a la cerca de mitjanes ponderades. En segon lloc, un marc de treball específicament dissenyat per a generar correspondències al camp del registre d'imatges s'ha modelat, on es considera que una de les imatges és una imatge completa, i l'altra és una mostra petita d'aquesta. La conclusió presenta noves percepcions de com el nostre marc de treball de consens pot ser millorada, i com els dos desenvolupaments paral·lels poden convergir amb el marc de treball de consens.En esta tesis presentamos un marco de trabajo para aprender el consenso dadas múltiples correspondencias. Se asume que las distintas partes involucradas han generado dichas correspondencias por separado, y nuestro sistema actúa como un mecanismo que calibra distintas características y considera diferentes parámetros para aprender las mejores asignaciones y así, conformar una correspondencia con la mayor precisión posible a expensas de un costo computacional razonable. El marco de trabajo de consenso es presentado en una forma gradual, comenzando por los acercamientos más básicos que utilizaban exclusivamente conceptos bien definidos o únicamente un par de correspondencias, hasta el modelo final que es capaz de considerar múltiples correspondencias, con la capacidad de aprender automáticamente algunos parámetros de ponderación. Cada paso de este marco de trabajo es evaluado usando bases de datos de naturaleza variada para demostrar efectivamente que es posible tratar diferentes escenarios de matching. Adicionalmente, dos avances suplementarios relacionados con correspondencias son presentados en este trabajo. En primer lugar, una nueva métrica de distancia para correspondencias ha sido desarrollada, la cual derivó en una nueva estrategia para la búsqueda de medias ponderadas. En segundo lugar, un marco de trabajo específicamente diseñado para generar correspondencias en el campo del registro de imágenes ha sido establecida, donde se considera que una de las imágenes es una imagen completa, y la otra es una muestra pequeña de ésta. La conclusión presenta nuevas percepciones de cómo nuestro marco de trabajo de consenso puede ser mejorada, y cómo los dos desarrollos paralelos pueden converger con éste.In this work, we present a framework to learn the consensus given multiple correspondences. It is assumed that the several parties involved have generated separately these correspondences, and our system acts as a mechanism that gauges several characteristics and considers different parameters to learn the best mappings and thus, conform a correspondence with the highest possible accuracy at the expense of a reasonable computational cost. The consensus framework is presented in a gradual form, starting from the most basic approaches that used exclusively well-known concepts or only two correspondences, until the final model which is able to consider multiple correspondences, with the capability of automatically learning some weighting parameters. Each step of the framework is evaluated using databases of varied nature to effectively demonstrate that it is capable to address different matching scenarios. In addition, two supplementary advances related on correspondences are presented in this work. Firstly, a new distance metric for correspondences has been developed, which lead to a new strategy for the weighted mean correspondence search. Secondly, a framework specifically designed for correspondence generation in the image registration field has been established, where it is considered that one of the images is a full image, and the other one is a small sample of it. The conclusion presents insights of how our consensus framework can be enhanced, and how these two parallel developments can converge with it