Suchbasierte automatische Bildannotation anhand geokodierter Community-Fotos

In the Web 2.0 era, platforms for sharing and collaboratively annotating images with keywords, called tags, became very popular. Tags are a powerful means for organizing and retrieving photos. However, manual tagging is time consuming. Recently, the sheer amount of user-tagged photos available on the Web encouraged researchers to explore new techniques for automatic image annotation. The idea is to annotate an unlabeled image by propagating the labels of community photos that are visually similar to it. Most recently, an ever increasing amount of community photos is also associated with location information, i.e., geotagged. In this thesis, we aim at exploiting the location context and propose an approach for automatically annotating geotagged photos. Our objective is to address the main limitations of state-of-the-art approaches in terms of the quality of the produced tags and the speed of the complete annotation process. To achieve these goals, we, first, deal with the problem of collecting images with the associated metadata from online repositories. Accordingly, we introduce a strategy for data crawling that takes advantage of location information and the social relationships among the contributors of the photos. To improve the quality of the collected user-tags, we present a method for resolving their ambiguity based on tag relatedness information. In this respect, we propose an approach for representing tags as probability distributions based on the algorithm of Laplacian score feature selection. Furthermore, we propose a new metric for calculating the distance between tag probability distributions by extending Jensen-Shannon Divergence to account for statistical fluctuations. To efficiently identify the visual neighbors, the thesis introduces two extensions to the state-of-the-art image matching algorithm, known as Speeded Up Robust Features (SURF). To speed up the matching, we present a solution for reducing the number of compared SURF descriptors based on classification techniques, while the accuracy of SURF is improved through an efficient method for iterative image matching. Furthermore, we propose a statistical model for ranking the mined annotations according to their relevance to the target image. This is achieved by combining multi-modal information in a statistical framework based on Bayes' rule. Finally, the effectiveness of each of mentioned contributions as well as the complete automatic annotation process are evaluated experimentally.Seit der Einführung von Web 2.0 steigt die Popularität von Plattformen, auf denen Bilder geteilt und durch die Gemeinschaft mit Schlagwörtern, sogenannten Tags, annotiert werden. Mit Tags lassen sich Fotos leichter organisieren und auffinden. Manuelles Taggen ist allerdings sehr zeitintensiv. Animiert von der schieren Menge an im Web zugänglichen, von Usern getaggten Fotos, erforschen Wissenschaftler derzeit neue Techniken der automatischen Bildannotation. Dahinter steht die Idee, ein noch nicht beschriftetes Bild auf der Grundlage visuell ähnlicher, bereits beschrifteter Community-Fotos zu annotieren. Unlängst wurde eine immer größere Menge an Community-Fotos mit geographischen Koordinaten versehen (geottagged). Die Arbeit macht sich diesen geographischen Kontext zunutze und präsentiert einen Ansatz zur automatischen Annotation geogetaggter Fotos. Ziel ist es, die wesentlichen Grenzen der bisher bekannten Ansätze in Hinsicht auf die Qualität der produzierten Tags und die Geschwindigkeit des gesamten Annotationsprozesses aufzuzeigen. Um dieses Ziel zu erreichen, wurden zunächst Bilder mit entsprechenden Metadaten aus den Online-Quellen gesammelt. Darauf basierend, wird eine Strategie zur Datensammlung eingeführt, die sich sowohl der geographischen Informationen als auch der sozialen Verbindungen zwischen denjenigen, die die Fotos zur Verfügung stellen, bedient. Um die Qualität der gesammelten User-Tags zu verbessern, wird eine Methode zur Auflösung ihrer Ambiguität vorgestellt, die auf der Information der Tag-Ähnlichkeiten basiert. In diesem Zusammenhang wird ein Ansatz zur Darstellung von Tags als Wahrscheinlichkeitsverteilungen vorgeschlagen, der auf den Algorithmus der sogenannten Laplacian Score (LS) aufbaut. Des Weiteren wird eine Erweiterung der Jensen-Shannon-Divergence (JSD) vorgestellt, die statistische Fluktuationen berücksichtigt. Zur effizienten Identifikation der visuellen Nachbarn werden in der Arbeit zwei Erweiterungen des Speeded Up Robust Features (SURF)-Algorithmus vorgestellt. Zur Beschleunigung des Abgleichs wird eine Lösung auf der Basis von Klassifikationstechniken präsentiert, die die Anzahl der miteinander verglichenen SURF-Deskriptoren minimiert, während die SURF-Genauigkeit durch eine effiziente Methode des schrittweisen Bildabgleichs verbessert wird. Des Weiteren wird ein statistisches Modell basierend auf der Baye'schen Regel vorgeschlagen, um die erlangten Annotationen entsprechend ihrer Relevanz in Bezug auf das Zielbild zu ranken. Schließlich wird die Effizienz jedes einzelnen, erwähnten Beitrags experimentell evaluiert. Darüber hinaus wird die Performanz des vorgeschlagenen automatischen Annotationsansatzes durch umfassende experimentelle Studien als Ganzes demonstriert

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Personalized Recommendations Based On Users’ Information-Centered Social Networks

The overwhelming amount of information available today makes it difficult for users to find useful information and as the solution to this information glut problem, recommendation technologies emerged. Among the several streams of related research, one important evolution in technology is to generate recommendations based on users’ own social networks. The idea to take advantage of users’ social networks as a foundation for their personalized recommendations evolved from an Internet trend that is too important to neglect – the explosive growth of online social networks. In spite of the widely available and diversified assortment of online social networks, most recent social network-based recommendations have concentrated on limited kinds of online sociality (i.e., trust-based networks and online friendships). Thus, this study tried to prove the expandability of social network-based recommendations to more diverse and less focused social networks. The online social networks considered in this dissertation include: 1) a watching network, 2) a group membership, and 3) an academic collaboration network. Specifically, this dissertation aims to check the value of users’ various online social connections as information sources and to explore how to include them as a foundation for personalized recommendations. In our results, users in online social networks shared similar interests with their social partners. An in-depth analysis about the shared interests indicated that online social networks have significant value as a useful information source. Through the recommendations generated by the preferences of social connection, the feasibility of users’ social connections as a useful information source was also investigated comprehensively. The social network-based recommendations produced as good as, or sometimes better, suggestions than traditional collaborative filtering recommendations. Social network-based recommendations were also a good solution for the cold-start user problem. Therefore, in order for cold-start users to receive reasonably good recommendations, it is more effective to be socially associated with other users, rather than collecting a few more items. To conclude, this study demonstrates the viability of multiple social networks as a means for gathering useful information and addresses how different social networks of a novelty value can improve upon conventional personalization technology

AXMEDIS 2008

The AXMEDIS International Conference series aims to explore all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, protection and rights management, to address the latest developments and future trends of the technologies and their applications, impacts and exploitation. The AXMEDIS events offer venues for exchanging concepts, requirements, prototypes, research ideas, and findings which could contribute to academic research and also benefit business and industrial communities. In the Internet as well as in the digital era, cross-media production and distribution represent key developments and innovations that are fostered by emergent technologies to ensure better value for money while optimising productivity and market coverage

ECLAP 2012 Conference on Information Technologies for Performing Arts, Media Access and Entertainment

It has been a long history of Information Technology innovations within the Cultural Heritage areas. The Performing arts has also been enforced with a number of new innovations which unveil a range of synergies and possibilities. Most of the technologies and innovations produced for digital libraries, media entertainment and education can be exploited in the field of performing arts, with adaptation and repurposing. Performing arts offer many interesting challenges and opportunities for research and innovations and exploitation of cutting edge research results from interdisciplinary areas. For these reasons, the ECLAP 2012 can be regarded as a continuation of past conferences such as AXMEDIS and WEDELMUSIC (both pressed by IEEE and FUP). ECLAP is an European Commission project to create a social network and media access service for performing arts institutions in Europe, to create the e-library of performing arts, exploiting innovative solutions coming from the ICT