Clustering and Community Detection in Directed Networks: A Survey

Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed - in the sense that there is directionality on the edges, making the semantics of the edges non symmetric. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of applications. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs - with clustering being the primary method and tool for community detection and evaluation. The goal of this paper is to offer an in-depth review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms capitalize on. Then we present the relevant work along two orthogonal classifications. The first one is mostly concerned with the methodological principles of the clustering algorithms, while the second one approaches the methods from the viewpoint regarding the properties of a good cluster in a directed network. Further, we present methods and metrics for evaluating graph clustering results, demonstrate interesting application domains and provide promising future research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear

Image processing by region extraction using a clustering approach based on color

This thesis describes an image segmentation technique based on watersheds, a clustering technique which does not use spatial information, but relies on multispectral images. These are captured using a monochrome camera and narrow-band filters; we call this color segmentation, although it does not use color in a physiological sense. A major part of the work is testing the method developed using different color images. Starting with a general discussion of image processing, the different techniques used in image segmentation are reviewed, and the application of mathematical morphology to image processing is discussed. The use of watersheds as a clustering technique in two- dimensional color space is discussed, and system performance illustrated. The method can be improved for industrial applications by using normalized color to eliminate the problem of shadows. These methods are extended to segment the image into regions recursively. Different types of color images including both man made color images, and natural color images have been used to illustrate performance. There is a brief discussion and a simple illustration showing how segmentation can be used in image compression, and of the application of pyramidal data structures in clustering for coarse segmentation. The thesis concludes with an investigation of the methods which can be used to improve these segmentation results. This includes edge extraction, texture extraction, and recursive merging

A COMPUTATION METHOD/FRAMEWORK FOR HIGH LEVEL VIDEO CONTENT ANALYSIS AND SEGMENTATION USING AFFECTIVE LEVEL INFORMATION

VIDEO segmentation facilitates e±cient video indexing and navigation in large digital video archives. It is an important process in a content-based video indexing and retrieval (CBVIR) system. Many automated solutions performed seg- mentation by utilizing information about the \facts" of the video. These \facts" come in the form of labels that describe the objects which are captured by the cam- era. This type of solutions was able to achieve good and consistent results for some video genres such as news programs and informational presentations. The content format of this type of videos is generally quite standard, and automated solutions were designed to follow these format rules. For example in [1], the presence of news anchor persons was used as a cue to determine the start and end of a meaningful news segment. The same cannot be said for video genres such as movies and feature films. This is because makers of this type of videos utilized different filming techniques to design their videos in order to elicit certain affective response from their targeted audience. Humans usually perform manual video segmentation by trying to relate changes in time and locale to discontinuities in meaning [2]. As a result, viewers usually have doubts about the boundary locations of a meaningful video segment due to their different affective responses. This thesis presents an entirely new view to the problem of high level video segmentation. We developed a novel probabilistic method for affective level video content analysis and segmentation. Our method had two stages. In the first stage, a®ective content labels were assigned to video shots by means of a dynamic bayesian 0. Abstract 3 network (DBN). A novel hierarchical-coupled dynamic bayesian network (HCDBN) topology was proposed for this stage. The topology was based on the pleasure- arousal-dominance (P-A-D) model of a®ect representation [3]. In principle, this model can represent a large number of emotions. In the second stage, the visual, audio and a®ective information of the video was used to compute a statistical feature vector to represent the content of each shot. Affective level video segmentation was achieved by applying spectral clustering to the feature vectors. We evaluated the first stage of our proposal by comparing its emotion detec- tion ability with all the existing works which are related to the field of a®ective video content analysis. To evaluate the second stage, we used the time adaptive clustering (TAC) algorithm as our performance benchmark. The TAC algorithm was the best high level video segmentation method [2]. However, it is a very computationally intensive algorithm. To accelerate its computation speed, we developed a modified TAC (modTAC) algorithm which was designed to be mapped easily onto a field programmable gate array (FPGA) device. Both the TAC and modTAC algorithms were used as performance benchmarks for our proposed method. Since affective video content is a perceptual concept, the segmentation per- formance and human agreement rates were used as our evaluation criteria. To obtain our ground truth data and viewer agreement rates, a pilot panel study which was based on the work of Gross et al. [4] was conducted. Experiment results will show the feasibility of our proposed method. For the first stage of our proposal, our experiment results will show that an average improvement of as high as 38% was achieved over previous works. As for the second stage, an improvement of as high as 37% was achieved over the TAC algorithm

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Methods for 3D Geometry Processing in the Cultural Heritage Domain

This thesis presents methods for 3D geometry processing under the aspects of cultural heritage applications. After a short overview over the relevant basics in 3D geometry processing, the present thesis investigates the digital acquisition of 3D models. A particular challenge in this context are on the one hand difficult surface or material properties of the model to be captured. On the other hand, the fully automatic reconstruction of models even with suitable surface properties that can be captured with Laser range scanners is not yet completely solved. This thesis presents two approaches to tackle these challenges. One exploits a thorough capture of the object’s appearance and a coarse reconstruction for a concise and realistic object representation even for objects with problematic surface properties like reflectivity and transparency. The other method concentrates on digitisation via Laser-range scanners and exploits 2D colour images that are typically recorded with the range images for a fully automatic registration technique. After reconstruction, the captured models are often still incomplete, exhibit holes and/or regions of insufficient sampling. In addition to that, holes are often deliberately introduced into a registered model to remove some undesired or defective surface part. In order to produce a visually appealing model, for instance for visualisation purposes, for prototype or replica production, these holes have to be detected and filled. Although completion is a well-established research field in 2D image processing and many approaches do exist for image completion, surface completion in 3D is a fairly new field of research. This thesis presents a hierarchical completion approach that employs and extends successful exemplar-based 2D image processing approaches to 3D and fills in detail-equipped surface patches into missing surface regions. In order to identify and construct suitable surface patches, selfsimilarity and coherence properties of the surface context of the hole are exploited. In addition to the reconstruction and repair, the present thesis also investigates methods for a modification of captured models via interactive modelling. In this context, modelling is regarded as a creative process, for instance for animation purposes. On the other hand, it is also demonstrated how this creative process can be used to introduce human expertise into the otherwise automatic completion process. This way, reconstructions are feasible even of objects where already the data source, the object itself, is incomplete due to corrosion, demolition, or decay.Methoden zur 3D-Geometrieverarbeitung im Kulturerbesektor In dieser Arbeit werden Methoden zur Bearbeitung von digitaler 3D-Geometrie unter besonderer Berücksichtigung des Anwendungsbereichs im Kulturerbesektor vorgestellt. Nach einem kurzen Überblick über die relevanten Grundlagen der dreidimensionalen Geometriebehandlung wird zunächst die digitale Akquise von dreidimensionalen Objekten untersucht. Eine besondere Herausforderung stellen bei der Erfassung einerseits ungünstige Oberflächen- oder Materialeigenschaften der Objekte dar (wie z.B. Reflexivität oder Transparenz), andererseits ist auch die vollautomatische Rekonstruktion von solchen Modellen, die sich verhältnismäßig problemlos mit Laser-Range Scannern erfassen lassen, immer noch nicht vollständig gelöst. Daher bilden zwei neuartige Verfahren, die diesen Herausforderungen begegnen, den Anfang. Auch nach der Registrierung sind die erfassten Datensätze in vielen Fällen unvollständig, weisen Löcher oder nicht ausreichend abgetastete Regionen auf. Darüber hinaus werden in vielen Anwendungen auch, z.B. durch Entfernen unerwünschter Oberflächenregionen, Löcher gewollt hinzugefügt. Für eine optisch ansprechende Rekonstruktion, vor allem zu Visualisierungszwecken, im Bildungs- oder Unterhaltungssektor oder zur Prototyp- und Replik-Erzeugung müssen diese Löcher zunächst automatisch detektiert und anschließend geschlossen werden. Obwohl dies im zweidimensionalen Fall der Bildbearbeitung bereits ein gut untersuchtes Forschungsfeld darstellt und vielfältige Ansätze zur automatischen Bildvervollständigung existieren, ist die Lage im dreidimensionalen Fall anders, und die Übertragung von zweidimensionalen Ansätzen in den 3D stellt vielfach eine große Herausforderung dar, die bislang keine zufriedenstellenden Lösungen erlaubt hat. Nichtsdestoweniger wird in dieser Arbeit ein hierarchisches Verfahren vorgestellt, das beispielbasierte Konzepte aus dem 2D aufgreift und Löcher in Oberflächen im 3D unter Ausnutzung von Selbstähnlichkeiten und Kohärenzeigenschaften des Oberflächenkontextes schließt. Um plausible Oberflächen zu erzeugen werden die Löcher dabei nicht nur glatt gefüllt, sondern auch feinere Details aus dem Kontext rekonstruiert. Abschließend untersucht die vorliegende Arbeit noch die Modifikation der vervollständigten Objekte durch Freiformmodellierung. Dies wird dabei zum einen als kreativer Prozess z.B. zu Animationszwecken betrachtet. Zum anderen wird aber auch untersucht, wie dieser kreative Prozess benutzt werden kann, um etwaig vorhandenes Expertenwissen in die ansonsten automatische Vervollständigung mit einfließen zu lassen. Auf diese Weise werden auch Rekonstruktionen ermöglicht von Objekten, bei denen schon die Datenquelle, also das Objekt selbst z.B. durch Korrosion oder mutwillige Zerstörung unvollständig ist

Representing complex data using localized principal components with application to astronomical data

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex''. In these cases, simple principal component analysis (PCA) as a tool for dimension reduction can fail badly. Of the many alternative approaches proposed so far, local approximations of PCA are among the most promising. This paper will give a short review of localized versions of PCA, focusing on local principal curves and local partitioning algorithms. Furthermore we discuss projections other than the local principal components. When performing local dimension reduction for regression or classification problems it is important to focus not only on the manifold structure of the covariates, but also on the response variable(s). Local principal components only achieve the former, whereas localized regression approaches concentrate on the latter. Local projection directions derived from the partial least squares (PLS) algorithm offer an interesting trade-off between these two objectives. We apply these methods to several real data sets. In particular, we consider simulated astrophysical data from the future Galactic survey mission Gaia.Comment: 25 pages. In "Principal Manifolds for Data Visualization and Dimension Reduction", A. Gorban, B. Kegl, D. Wunsch, and A. Zinovyev (eds), Lecture Notes in Computational Science and Engineering, Springer, 2007, pp. 180--204, http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-173750210-

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun