Mitarbeiter Lehrstuhl/Einrichtung der Fakultät für Informatik und Mathematik
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In this thesis, we investigates plane drawings of undirected and directed graphs on cylinder surfaces. In the case of undirected graphs, the vertices are positioned on a line that is parallel to the cylinder’s axis and the edge curves must not intersect this line. We show that a plane drawing is possible if and only if the graph is a double-ended queue (deque) graph, i. e., the vertices of the graph can be processed according to a linear order and the edges correspond to items in the deque inserted and removed at their end vertices. A surprising consequence resulting from these observations is that the deque characterizes planar graphs with a Hamiltonian path. This result extends the known characterization of planar graphs with a Hamiltonian cycle by two stacks. By these insights, we also obtain a new characterization of queue graphs and their duals. We also consider the complexity of deciding whether a graph is a deque graph and prove that it is NP-complete. By introducing a split operation, we obtain the splittable deque and show that it characterizes planarity. For the proof, we devise an algorithm that uses the splittable deque to test whether a rotation system is planar. In the case of directed graphs, we study upward plane drawings where the edge curves follow the direction of the cylinder’s axis (standing upward planarity; SUP) or they wind around the axis (rolling upward planarity; RUP). We characterize RUP graphs by means of their duals and show that RUP and SUP swap their roles when considering a graph and its dual. There is a physical interpretation underlying this characterization: A SUP graph is to its RUP dual graph as electric current passing through a conductor to the magnetic field surrounding the conductor. Whereas testing whether a graph is RUP is NP-hard in general [Bra14], for directed graphs without sources and sink, we develop a linear-time recognition algorithm that is based on our dual graph characterization of RUP graphs.
Modern Web technology makes the dream of fully interactive and enriched video come true. Nowadays it is possible to organize videos in a non-linear way playing in a sequence unknown in advance. Furthermore, additional information can be added to the video, ranging from short descriptions to animated images and further videos. This affords an easy and efficient to use authoring tool which is capable of the management of the single media objects, as well as a clear arrangement of the links between the parts. Tools of this kind can be found rarely and do mostly not provide the full range of needed functions. While providing an interactive experience to the viewer in the Web player, parallel plot sequences and additional information lead to an increased download volume. This may cause pauses during playback while elements have to be downloaded which are displayed with the video. A good quality of experience for these videos with small waiting times and a playback without interruptions is desired. This work presents the SIVA Suite to create the previously described annotated interactive non-linear videos. We propose a video model for interactivity, non-linearity, and annotations, which is implemented in an XML format, an authoring tool, and a player. Video is the main medium, whereby different scenes are linked to a scene graph. Time controlled additional content called annotations, like text, images, audio files, or videos, is added to the scenes. The user is able to navigate in the scene graph by selecting a button at a button panel. Furthermore, other navigational elements like a table of contents or a keyword search are provided. Besides the SIVA Suite, this thesis presents algorithms and strategies for download and cache management to provide a good quality of experience while watching the annotated interactive non-linear videos. Therefor, we implemented a standard-independent player framework. Integrated into a simulation environment, the framework allows to evaluate algorithms and strategies for the calculation of start-up times, and the selection of elements to pre-fetch into and delete from the cache. Their interaction during the playback of non-linear video contents can be analyzed. The algorithms and strategies can be used to minimize interruptions in the video flow after user interactions. Our extensive evaluation showed that our techniques result in faster start-up times and lesser interruptions in the video flow than those of other players. Knowledge of the structure of an interactive non-linear video can be used to minimize the start-up time at the beginning of a video while minimizing an increase in the overall download volume.
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.
UME is the notion that a user should receive informative adapted content anytime and anywhere. Personalization of videos, which adapts their content according to user preferences, is a vital aspect of achieving the UME vision. User preferences can be translated into several types of constraints that must be considered by the adaptation process, including semantic constraints directly related to the content of the video. To deal with these semantic constraints, a fine-grained adaptation, which can go down to the level of video objects, is necessary. The overall goal of this adaptation process is to provide users with adapted content that maximizes their Quality of Experience (QoE). This QoE depends at the same time on the level of the user's satisfaction in perceiving the adapted content, the amount of knowledge assimilated by the user, and the adaptation execution time. In video adaptation frameworks, the Adaptation Decision Taking Engine (ADTE), which can be considered as the "brain" of the adaptation engine, is responsible for achieving this goal. The task of the ADTE is challenging as many adaptation operations can satisfy the same semantic constraint, and thus arising in several feasible adaptation plans. Indeed, for each entity undergoing the adaptation process, the ADTE must decide on the adequate adaptation operator that satisfies the user's preferences while maximizing his/her quality of experience. The first challenge to achieve in this is to objectively measure the quality of the adapted video, taking into consideration the multiple aspects of the QoE. The second challenge is to assess beforehand this quality in order to choose the most appropriate adaptation plan among all possible plans. The third challenge is to resolve conflicting or overlapping semantic constraints, in particular conflicts arising from constraints expressed by owner's intellectual property rights about the modification of the content. In this thesis, we tackled the aforementioned challenges by proposing a Utility Function (UF), which integrates semantic concerns with user's perceptual considerations. This UF models the relationships among adaptation operations, user preferences, and the quality of the video content. We integrated this UF into an ADTE. This ADTE performs a multi-level piecewise reasoning to choose the adaptation plan that maximizes the user-perceived quality. Furthermore, we included intellectual property rights in the adaptation process. Thereby, we modeled content owner constraints. We dealt with the problem of conflicting user and owner constraints by mapping it to a known optimization problem. Moreover, we developed the SVCAT, which produces structural and high-level semantic annotation according to an original object-based video content model. We modeled as well the user's preferences proposing extensions to MPEG-7 and MPEG-21. All the developed contributions were carried out as part of a coherent framework called PIAF. PIAF is a complete modular MPEG standard compliant framework that covers the whole process of semantic video adaptation. We validated this research with qualitative and quantitative evaluations, which assess the performance and the efficiency of the proposed adaptation decision-taking engine within PIAF. The experimental results show that the proposed UF has a high correlation with subjective video quality evaluation.
Multimedia retrieval is an essential part of today's world. This situation is observable in industrial domains, e.g., medical imaging, as well as in the private sector, visible by activities in manifold Social Media platforms. This trend led to the creation of a huge environment of multimedia information retrieval services offering multimedia resources for almost any user requests. Indeed, the encompassed data is in general retrievable by (proprietary) APIs and query languages, but unfortunately a unified access is not given due to arising interoperability issues between those services. In this regard, this thesis focuses on two application scenarios, namely a medical retrieval system supporting a radiologist's workflow, as well as an interoperable image retrieval service interconnecting diverse data silos. The scientific contribution of this dissertation is split in three different parts: the first part of this thesis improves the metadata interoperability issue. Here, major contributions to a community-driven, international standardization have been proposed leading to the specification of an API and ontology to enable a unified annotation and retrieval of media resources. The second part issues a metasearch engine especially designed for unified retrieval in distributed and heterogeneous multimedia retrieval environments. This metasearch engine is capable of being operated in a federated as well as autonomous manner inside the aforementioned application scenarios. The remaining third part ensures an efficient retrieval due to the integration of optimization techniques for multimedia retrieval in the overall query execution process of the metasearch engine.