[727] | Andreas Leibetseder, Klaus Schöffmann, Extracting and Using Medical Expert Knowledge to Advance in Video Processing for Gynecologic Endoscopy, In ICMR '18 Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, ACM Digital Library, New York, NY, 2018.
[bib][url] [doi] [abstract]
Abstract: Modern day endoscopic technology enables medical staff to conveniently document surgeries via recording raw treatment footage, which can be utilized for planning further proceedings, future case revisitations or even educational purposes. However, the prospect of manually perusing recorded media files constitutes a tedious additional workload on physicians' already packed timetables and therefore ultimately represents a burden rather than a benefit. The aim of this PhD project is to improve upon this situation by closely collaborating with medical experts in order to devise datasets and systems to facilitate semi-automatic post-surgical media processing.
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[726] | Andreas Leibetseder, Stefan Petscharnig, Manfred Jürgen Primus, Sabrina Kletz, Bernd Münzer, Klaus Schöffmann, Jörg Keckstein, Lapgyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecology, In MMSys '18 Proceedings of the 9th ACM Multimedia Systems Conference, ACM Press, New York (NY), pp. 357-362, 2018.
[bib][url] [doi] [abstract]
Abstract: Modern imaging technology enables medical practitioners to perform minimally invasive surgery (MIS), i.e. a variety of medical interventions inflicting minimal trauma upon patients, hence, greatly improving their recoveries. Not only patients but also surgeons can benefit from this technology, as recorded media can be utilized for speeding-up tedious and time-consuming tasks such as treatment planning or case documentation. In order to improve the predominantly manually conducted process of analyzing said media, with this work we publish four datasets extracted from gynecologic, laparoscopic interventions with the intend on encouraging research in the field of post-surgical automatic media analysis. These datasets are designed with the following use cases in mind: medical image retrieval based on a query image, detection of instrument counts, surgical actions and anatomical structures, as well as distinguishing on which anatomical structure a certain action is performed. Furthermore, we provide suggestions for evaluation metrics and first baseline experiments.
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[725] | Andreas Leibetseder, Sabrina Kletz, Klaus Schöffmann, Sketch-Based Similarity Search for Collaborative Feature Maps, In MultiMedia Modeling - 24th International Conference, MMM 2018 (Part 2) (Klaus Schöffmann, Thanarat H. Chalidabhongse, Chong-Wah Ngo, Supavadee Aramvith, Noel E. O´Connor, Yo-Sung Ho, Moncef Gabbouj, Ahmed Elgammal, eds.), Springer, vol. 10705, Berlin, pp. 425-430, 2018.
[bib][url] [doi] [abstract]
Abstract: Past editions of the annual Video Browser Showdown (VBS) event have brought forward many tools targeting a diverse amount of techniques for interactive video search, among which sketch-based search showed promising results. Aiming at exploring this direction further, we present a custom approach for tackling the problem of finding similarities in the TRECVID IACC.3 dataset via hand-drawn pictures using color compositions together with contour matching. The proposed methodology is integrated into the established Collaborative Feature Maps (CFM) system, which has first been utilized in the VBS 2017 challenge.
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[724] | Andreas Leibetseder, Manfred Jürgen Primus, Klaus Schöffmann, Automatic Smoke Classification in Endoscopic Video, In MultiMedia Modeling - 24th International Conference, MMM 2018 (Part 2) (Klaus Schöffmann, Thanarat H. Chalidabhongse, Chong-Wah Ngo, Supavadee Aramvith, Noel E. O´Connor, Yo-Sung Ho, Moncef Gabbouj, Ahmed Elgammal, eds.), Springer, vol. 10705, Berlin, pp. 362-366, 2018.
[bib][url] [doi] [abstract]
Abstract: Medical smoke evacuation systems enable proper, filtered removal of toxic fumes during surgery, while stabilizing internal pressure during endoscopic interventions. Typically activated manually, they, however, are prone to inefficient utilization: tardy activation enables smoke to interfere with ongoing surgeries and late deactivation wastes precious resources. In order to address such issues, in this work we demonstrate a vision-based tool indicating endoscopic smoke – a first step towards automatic activation of said systems and avoiding human misconduct. In the back-end we employ a pre-trained convolutional neural network (CNN) model for distinguishing images containing smoke from others.
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[723] | Sabrina Kletz, Andreas Leibetseder, Klaus Schöffmann, Evaluation of Visual Content Descriptors for Supporting Ad-Hoc Video Search Tasks at the Video Browser Showdown, In MultiMedia Modeling - 24th International Conference, MMM 2018 (Part 1) (Klaus Schöffmann, Thanarat H. Chalidabhongse, Chong-Wah Ngo, Noel E. O´Connor, Supavadee Aramvith, Yo-Sung Ho, Moncef Gabbouj, Ahmed Elgammal, eds.), Springer, vol. 10704, Berlin, pp. 203-215, 2018.
[bib][url] [doi] [abstract]
Abstract: Since 2017 the Video Browser Showdown (VBS) collaborates with TRECVID and interactively evaluates Ad-Hoc Video Search (AVS) tasks, in addition to Known-Item Search (KIS) tasks. In this video search competition the participants have to find relevant target scenes to a given textual query within a specific time limit, in a large dataset consisting of 600 h of video content. Since usually the number of relevant scenes for such an AVS query is rather high, the teams at the VBS 2017 could find only a small portion of them. One way to support them at the interactive search would be to automatically retrieve other similar instances of an already found target scene. However, it is unclear which content descriptors should be used for such an automatic video content search, using a query-by-example approach. Therefore, in this paper we investigate several different visual content descriptors (CNN Features, CEDD, COMO, HOG, Feature Signatures and HOF) for the purpose of similarity search in the TRECVID IACC.3 dataset, used for the VBS. Our evaluation shows that there is no single descriptor that works best for every AVS query, however, when considering the total performance over all 30 AVS tasks of TRECVID 2016, CNN features provide the best performance.
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[722] | Dragi Kimovski, Humaira Ijaz, Nishant Saurabh, Radu Prodan, An Adaptive Nature-inspired Fog Architecture, In 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC 2018), IEEE, Piscataway (NJ), 2018.
[bib][url] [doi] [abstract]
Abstract: During the last decade, Cloud computing has efficiently exploited the economyof scale by providing low cost computational and storage resources over theInternet, eventually leading to consolidation of computing resources into largedata centers. However, the nascent of the highly decentralized Internet ofThings (IoT) technologies that cannot effectively utilize the centralized Cloudinfrastructures pushes computing towards resource dispersion. Fog computingextends the Cloud paradigm by enabling dispersion of the computational andstorage resources at the edge of the network in a close proximity to where thedata is generated. In its essence, Fog computing facilitates the operation ofthe limited compute, storage and networking resources physically located closeto the edge devices. However, the shared complexity of the Fog and theinfluence of the recent IoT trends moving towards deploying and interconnectingextremely large sets of pervasive devices and sensors, requires exploration ofadaptive Fog architectural approaches capable of adapting and scaling inresponse to the unpredictable load patterns of the distributed IoTapplications. In this paper we introduce a promising new nature-inspired Fogarchitecture, named SmartFog, capable of providing low decision making latencyand adaptive resource management. By utilizing novel algorithms and techniquesfrom the fields of multi-criteria decision making, graph theory and machinelearning we model the Fog as a distributed intelligent processing system,therefore emulating the function of the human brain.
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[721] | Yasir Noman Khalid, Muhammad Aleem, Radu Prodan, Azhar Iqbal Muhammad, Muhammad Arshad Islam, E-OSched: a load balancing scheduler for heterogeneous multicores, In Journal of Supercomputing, 2018.
[bib][url] [doi] [abstract]
Abstract: The contemporary multicore era has adhered to the heterogeneous computing devices as one of the proficient platforms to execute compute-intensive applications. These heterogeneous devices are based on CPUs and GPUs. OpenCL is deemed as one of the industry standards to program heterogeneous machines. The conventional application scheduling mechanisms allocate most of the applications to GPUs while leaving CPU device underutilized. This underutilization of slower devices (such as CPU) often originates the sub-optimal performance of data-parallel applications in terms of load balance, execution time, and throughput. Moreover, multiple scheduled applications on a heterogeneous system further aggravate the problem of performance inefficiency. This paper is an attempt to evade the aforementioned deficiencies via initiating a novel scheduling strategy named OSched. An enhancement to the OSched named E-OSched is also part of this study. The OSched performs the resource-aware assignment of jobs to both CPUs and GPUs while ensuring a balanced load. The load balancing is achieved via contemplation on computational requirements of jobs and computing potential of a device. The load-balanced execution is beneficiary in terms of lower execution time, higher throughput, and improved utilization. The E-OSched reduces the magnitude of the main memory contention during concurrent job execution phase. The mathematical model of the proposed algorithms is evaluated by comparison of simulation results with different state-of-the-art scheduling heuristics. The results revealed that the proposed E-OSched has performed significantly well than the state-of-the-art scheduling heuristics by obtaining up to 8.09% improved execution time and up to 7.07% better throughput.
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[720] | Bogdan Ionescu, Henning Müller, Mauricio Villegas, Aöna Garcoa Secp de Herrera, Carsten Eickhoff, Vincent Andrearczyk, Yashin Dicente Cid, Vitali Liauchuk, Vassili Kovalev, Sadid H. Hasan, Yuan Ling, Oladimeji Farri, Joey Liu, Matthew Lungren, Duc-Tien Dang-Nguyen, Luca Piras, Michael Riegler, Liting Zhou, Mathias Lux, Cathal Gurrin, Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation, In Experimental IR Meets Multilinguality, Multimodality, and Interaction, Springer, vol. 11018, Berlin, 2018.
[bib][url] [doi] [abstract]
Abstract: This paper presents an overview of the ImageCLEF 2018 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) Labs 2018. ImageCLEF is an ongoing initiative (it started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval with the aim of providing information access to collections of images in various usage scenarios and domains. In 2018, the 16th edition of ImageCLEF ran three main tasks and a pilot task: (1) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based only on the figure image; (2) a tuberculosis task that aims at detecting the tuberculosis type, severity and drug resistance from CT (Computed Tomography) volumes of the lung; (3) a LifeLog task (videos, images and other sources) about daily activities understanding and moment retrieval, and (4) a pilot task on visual question answering where systems are tasked with answering medical questions. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks, shows an increasing interest in this benchmarking campaign.
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[719] | Tobias Hossfeld, Christian Timmerer, Quality of experience column: an introduction, In ACM SIGMultimedia Records, ACM Press, vol. 10, New York (NY), 2018.
[bib][url] [doi] [abstract]
Abstract: Research on Quality of Experience (QoE) has advanced significantly in recent years and attracts attention from various stakeholders. Different facets have been addressed by the research community like subjective user studies to identify QoE influence factors for particular applications like video streaming, QoE models to capture the effects of those influence factors on concrete applications, QoE monitoring approaches at the end user site but also within the network to assess QoE during service consumption and to provide means for QoE management for improved QoE. However, in order to progress in the area of QoE, new research directions have to be taken. The application of QoE in practice needs to consider the entire QoE eco-system and the stakeholders along the service delivery chain to the end user.
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[718] | Mohammad Hosseini, Christian Timmerer, Dynamic Adaptive Point Cloud Streaming, In PV '18 Proceedings of the 23rd Packet Video Workshop, ACM Press, New York (NY), pp. 25-30, 2018.
[bib][url] [doi] [abstract]
Abstract: High-quality point clouds have recently gained interest as an emerging form of representing immersive 3D graphics. Unfortunately, these 3D media are bulky and severely bandwidth intensive, which makes it difficult for streaming to resource-limited and mobile devices. This has called researchers to propose efficient and adaptive approaches for streaming of high-quality point clouds.In this paper, we run a pilot study towards dynamic adaptive point cloud streaming, and extend the concept of dynamic adaptive streaming over HTTP (DASH) towards DASH-PC, a dynamic adaptive bandwidth-efficient and view-aware point cloud streaming system. DASH-PC can tackle the huge bandwidth demands of dense point cloud streaming while at the same time can semantically link to human visual acuity to maintain high visual quality when needed. In order to describe the various quality representations, we propose multiple thinning approaches to spatially sub-sample point clouds in the 3D space, and design a DASH Media Presentation Description manifest speci.c for point cloud streaming. Our initial evaluations show that we can achieve signi.cant bandwidth and performance improvement on dense point cloud streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.
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[717] | Steven Alexander Hicks, Konstantin Pogorelov, Thomas de Lange, Mathias Lux, Mattis Jeppsson, Kristin Ranheim Randel, Sigrun L. Eskeland, Pal Halvorsen, Michael Riegler, Comprehensible reasoning and automated reporting of medical examinations based on deep learning analysis, In MMSys '18 Proceedings of the 9th ACM Multimedia Systems Conference, ACM Press, New York (NY), pp. 490-493, 2018.
[bib][url] [doi] [abstract]
Abstract: In the future, medical doctors will to an increasing degree be assisted by deep learning neural networks for disease detection during examinations of patients. In order to make qualified decisions, the black box of deep learning must be opened to increase the understanding of the reasoning behind the decision of the machine learning system. Furthermore, preparing reports after the examinations is a significant part of a doctors work-day, but if we already have a system dissecting the neural network for understanding, the same tool can be used for automatic report generation. In this demo, we describe a system that analyses medical videos from the gastrointestinal tract. Our system dissects the Tensorflow-based neural network to provide insights into the analysis and uses the resulting classification and rationale behind the classification to automatically generate an examination report for the patient's medical journal.
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[716] | Steven Alexander Hicks, Sigrun L. Eskeland, Mathias Lux, Thomas de Lange, Kristin Ranheim Randel, Mattis Jeppsson, Konstantin Pogorelov, Pal Halvorsen, Michael Riegler, Mimir: an automatic reporting and reasoning system for deep learning based analysis in the medical domain, In MMSys '18 Proceedings of the 9th ACM Multimedia Systems Conference, ACM Press, New York (NY), pp. 369-374, 2018.
[bib][url] [doi] [abstract]
Abstract: Automatic detection of diseases is a growing field of interest, and machine learning in form of deep learning neural networks are frequently explored as a potential tool for the medical video analysis. To both improve the "black box"-understanding and assist in the administrative duties of writing an examination report, we release an automated multimedia reporting software dissecting the neural network to learn the intermediate analysis steps, i.e., we are adding a new level of understanding and explainability by looking into the deep learning algorithms decision processes. The presented open-source software can be used for easy retrieval and reuse of data for automatic report generation, comparisons, teaching and research. As an example, we use live colonoscopy as a use case which is the gold standard examination of the large bowel, commonly performed for clinical and screening purposes. The added information has potentially a large value, and reuse of the data for the automatic reporting may potentially save the doctors large amounts of time.
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[715] | Evsen Yanmaz, Saeed Yahyanejad, Bernhard Rinner, Hermann Hellwagner, Christian Bettstetter, Drone networks: Communications, coordination, and sensing, In Ad Hoc Networks, Elsevier, vol. 68, Amsterdam, pp. 1-15, 2018.
[bib][url] [doi] [abstract]
Abstract: Small drones are being utilized in monitoring, transport, safety and disaster management, and other domains. Envisioning that drones form autonomous networks incorporated into the air traffic, we describe a high-level architecture for the design of a collaborative aerial system consisting of drones with on-board sensors and embedded processing, coordination, and networking capabilities. We implement a multi-drone system consisting of quadcopters and demonstrate its potential in disaster assistance, search and rescue, and aerial monitoring. Furthermore, we illustrate design challenges and present potential solutions based on the lessons learned so far.
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[714] | Daniela Pohl, Abdelhamid Bouchachia, Hermann Hellwagner, Batch-based active learning: Application to social media data for crisis management, In Expert Systems with Applications, Elsevier Ltd., vol. 93, Amsterdam, pp. 232-244, 2018.
[bib][url] [doi] [abstract]
Abstract: Classification of evolving data streams is a challenging task, which is suitably tackled with online learning approaches. Data is processed instantly requiring the learning machinery to (self-)adapt by adjusting its model. However for high velocity streams, it is usually difficult to obtain labeled samples to train the classification model. Hence, we propose a novel online batch-based active learning algorithm (OBAL) to perform the labeling. OBAL is developed for crisis management applications where data streams are generated by the social media community. OBAL is applied to discriminate relevant from irrelevant social media items. An emergency management user will be interactively queried to label chosen items. OBAL exploits the boundary items for which it is highly uncertain about their class and makes use of two classifiers: k-Nearest Neighbors (kNN) and Support Vector Machine (SVM). OBAL is equipped with a labeling budget and a set of uncertainty strategies to identify the items for labeling. An extensive analysis is carried out to show OBAL’s performance, the sensitivity of its parameters, and the contribution of the individual uncertainty strategies. Two types of datasets are used: synthetic and social media datasets related to crises. The empirical results illustrate that OBAL has a very good discrimination power.
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[713] | Chang Ge, Ning Wang, Wei Koong Chai, Hermann Hellwagner, QoE-Assured 4K HTTP Live Streaming via Transient Segment Holding at Mobile Edge, In IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 36, no. 8, pp. 1816-1830, 2018.
[bib][url] [doi] [pdf] [abstract]
Abstract: HTTP-based live streaming has become increasingly popular in recent years, and more users have started generating 4K live streams from their devices (e.g., mobile phones) through social-media service providers like Facebook or YouTube. If the audience is located far from a live stream source across the global Internet, TCP throughput becomes substantially suboptimal due to slow start and congestion control mechanisms. This is especially the case when the end-to-end content delivery path involves radio access network at the last mile. As a result, the data rate perceived by a mobile receiver may not meet the high requirement of 4K video streams, which causes deteriorated quality-of-experience (QoE). In this paper, we propose a scheme named edge-based transient holding of live segment (ETHLE), which addresses the above-mentioned issue by performing context-aware transient holding of video segments at the mobile edge with virtualized content caching capability. Through holding the minimum number of live video segments at the mobile edge cache in a context-aware manner, the ETHLE scheme is able to achieve seamless 4K live streaming experiences across the global Internet by eliminating buffering and substantially reducing initial startup delay and live stream latency. It has been deployed as a virtual network function at an LTE-A network, and its performance has been evaluated using real live stream sources that are distributed around the world. The significance of this paper is that by leveraging virtualized caching resources at the mobile edge, we address the conventional transport-layer bottleneck and enable QoE-assured Internet-wide live streaming services with high data rate requirements.
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[712] | Duc-Tien Dang-Nguyen, Klaus Schöffmann, Wolfgang Hürst, LSE2018 Panel - Challenges of Lifelog Search and Access, In LSC '18 Proceedings of the 2018 ACM Workshop on The Lifelog Search Challenge, ACM Digital Library, New York, NY, 2018.
[bib][url] [doi] [abstract]
Abstract: Lifelogging is becoming an increasingly important topic of research and this paper highlights the thoughts of the three panelists at the LSC - Lifelog Search Challenge at ICMR 2018 in Yokohama, Japan on June 11, 2018. The thoughts cover important topics such as the need for challenges in multimedia access, the need for a better user interface and the challenges in building datasets and organising benchmarking activities such as the LSC.
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[711] | Duc-Tien Dang-Nguyen, Luca Piras, Michael Riegler, Liting Zhou, Mathias Lux, Cathal Gurrin, Overview of ImageCLEFlifelog 2018: Daily Living Understanding andL ifelog Moment Retrieval, In CLEF 2018 Working Notes, CEUR-Workshop Proceedings, vol. 2125, 2018.
[bib][url] [abstract]
Abstract: Benchmarking in Multimedia and Retrieval related researchelds has a long tradition and important position within the community.Benchmarks such as the MediaEval Multimedia Benchmark or CLEFare well established and also served by the community. One major goalof these competitions beside of comparing dierent methods and approachesis also to create or promote new interesting research directionswithin multimedia. For example the Medico task at MediaEval with thegoal of medical related multimedia analysis. Although lifelogging createsa lot of attention in the community which is shown by several workshopsand special session hosted about the topic. Despite of that there exist alsosome lifelogging related benchmarks. For example the previous editionof the lifelogging task at ImageCLEF. The last years ImageCLEFlifelogtask was well received but had some barriers that made it dicult forsome researchers to participate (data size, multi modal features, etc.) TheImageCLEFlifelog 2018 tries to overcome these problems and make thetask accessible for an even broader audience (e.g., pre-extracted featuresare provided). Furthermore, the task is divided into two subtasks (challenges).The two challenges are lifelog moment retrieval (LMRT) and theActivities of Daily Living understanding (ADLT). All in all seven teamsparticipated with a total number of 41 runs which was an signicantincrease compared to the previous year.
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[710] | Abdelhak Bentaleb, Bayan Taani, Ali Cengiz Begen, Christian Timmerer, Roger Zimmermann, A Survey on Bitrate Adaptation Schemes for Streaming Media over HTTP, In IEEE Communications Surveys Tutorials, 2018.
[bib] [doi] |
[709] | Muhammad Aleem, Radu Prodan, On the Parallel Programmability of JavaSymphony for Multi-cores and Clusters, In International Journal of Ad Hoc and Ubiquitous Computing, 2018.
[bib][url] [doi] [abstract]
Abstract: This paper explains the programming aspects of a promising Java-based programming and execution framework called JavaSymphony. JavaSymphony provides unified high-level programming constructs for applications related to shared, distributed, hybrid memory parallel computers, and co-processors accelerators. JavaSymphony applications can be executed on a variety of multi-/many-core conventional and data-parallel architectures. JavaSymphony is based on the concept of dynamic virtual architectures, which allows programmers to define a hierarchical structure of the underlying computing resources and to control load-balancing and task-locality. In addition to GPU support, JavaSymphony provides a multi-core aware scheduling mechanism capable of mapping parallel applications on large multi-core machines and heterogeneous clusters. Several real applications and benchmarks (on modern multi-core computers, heterogeneous clusters, and machines consisting of a combination of different multi-core CPU and GPU devices) have been used to evaluate the performance. The results demonstrate that the JavaSymphony outperforms the Java implementations, as well as other modern alternative solutions.
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[708] | Konstantin Pogorelov, Michael Riegler, Pal Halvorsen, Carsten Griwodz, Thomas de Lange, Kristin Randel, Sigrun Eskeland, Duc-Tien Dang-Ngyuen, Olga Ostroukhova, Mathias Lux, Concetto Spampinato, A Comparison of Deep Learning with Global Features for Gastrointestinal Disease Detection, In Working Notes Proceedings of the MediaEval 2017 Workshop (Guillaume Gravier, Benjamin Bischke, Claire-Hélène Demarty, Maia Zaharieva, Michael Riegler, Emmanuel Dellandrea, Dmitry Bogdanov, Richard Sutcliffe, Gareth Jones, Martha Larson, eds.), CEUR Workshop Proceedings, Dublin, Ireland, pp. 3, 2017.
[bib][url] [abstract]
Abstract: This paper presents our approach for the 2017 Multimedia for Medicine Medico Task of the MediaEval 2017 Benchmark. We propose a system based on global features and deep neural networks, and preliminary results comparing the approaches are presented.
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[707] | Michael Riegler, Konstantin Pogorelov, Pal Halvorsen, Kristin Randel, Sigrun Eskeland, Duc-Tien Dang-Nguyen, Mathias Lux, Carsten Griwodz, Concetto Spampinato, Thomas de Lange, Multimedia for Medicine: The Medico Task at MediaEval 2017, In Working Notes Proceedings of the MediaEval 2017 Workshop (Guillaume Gravier, Benjamin Bischke, Claire-Hélène Demarty, Maia Zaharieva, Michael Riegler, Emmanuel Dellandrea, Dmitry Bogdanov, Richard Sutcliffe, Gareth Jones, Martha Larson, eds.), CEUR Workshop Proceedings, Dublin, Ireland, pp. 3, 2017.
[bib] [abstract]
Abstract: The Multimedia for Medicine Medico Task, running for the first time as part of MediaEval 2017, focuses on detecting abnormalities, diseases and anatomical landmarks in images captured by medical devices in the gastrointestinal tract. The task characteristics are described, including the use case and its challenges, the dataset with ground truth, the required participant runs and the evaluation metrics.
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[706] | Harald Beck, Bruno Bierbaumer, Minh Dao-Tran, Thomas Eiter, Hermann Hellwagner, Konstantin Schekotihin, Stream Reasoning-Based Control of Caching Strategies in CCN Routers, In Communications (ICC), 2017 IEEE International Conference on (Jean Luc Beylat, Hikmet Sari, eds.), IEEE, Paris, France, pp. 6, 2017.
[bib] [doi] [abstract]
Abstract: Routers in Content-Centric Networking (CCN) may locally cache frequently requested content in order to speed up delivery to end users. Thus, the issue of caching strategies arises, i.e., which content shall be stored and when it should be replaced. In this work, we employ, and study the feasibility of, novel techniques towards intelligent control of CCN routers that autonomously switch between existing caching strategies in response to changing content request patterns. In particular, we present a router architecture for CCN networks that is controlled by rule-based stream reasoning, following the recent formal framework LARS which extends Answer Set Programming for streams. The obtained possibility for flexible router configuration at runtime allows for versatile network control schemes and may help advance the further development of CCN. Moreover, the empirical evaluation of our feasibility study shows that the resulting caching agent may give significant performance gains.
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[705] | Mathias Lux, Michael Riegler, Glenn Macstravic, LireSolr: A Visual Information Retrieval Server, In ICMR '17 Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval (Nicu Sebe, Bogdan Ionescu, eds.), ACM, New Yor New York, USA, pp. 3, 2017.
[bib][url] [doi] [abstract]
Abstract: In this paper, we present LireSolr, an open source image retrieval server, build on top of the LIRE library and the Apache Solr search server. With LireSolr, visual information retrieval can be run on a server, which allows better distribution of workloads and simplifies applications in several areas including mobile and web. Furthermore, we showcase several example scenarios how LireSolr can be used to point out the broad range of possibilities and applications. The system is easy to install and setup, and the large number of retrieval tools either provided by LIRE or by other Apache Solr is made easily available on the search server. Moreover, our tool demonstrates how predictions from CNNs can easily be used to extend the visual information retrieval functionality.
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[704] | Anatoliy Zabrovskiy, Evgeny Petrov, Evgeny Kuzmin, Christian Timmerer, Evaluation of the Performance of Adaptive HTTP Streaming Systems, In arXiv.org [cs.MM], N.N., vol. abs/1710.02459, N.N., pp. 7, 2017.
[bib][url] [pdf] [abstract]
Abstract: Adaptive video streaming over HTTP is becoming omnipresent in our daily life. In the past, dozens of research papers have proposed novel approaches to address different aspects of adaptive streaming and a decent amount of player implementations (commercial and open source) are available. However, state of the art evaluations are sometimes superficial as many proposals only investigate a certain aspect of the problem or focus on a specific platform – player implementations used in actual services are rarely considered. HTML5 is now available on many platforms and foster the deployment of adaptive media streaming applications. We propose a common evaluation framework for adaptive HTML5 players and demonstrate its applicability by evaluating eight different players which are actually deployed in real-world services.
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[703] | Anatoliy Zabrovskiy, Evgeny Kuzmin, Evgeny Petrov, Christian Timmerer, Christopher Mueller, AdViSE: Adaptive Video Streaming Evaluation Framework for the Automated Testing of Media Players, In Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys'17) (Kuan-Ta Chen, ed.), ACM, New York, NY, USA, pp. 4, 2017.
[bib] [doi] [pdf] [abstract]
Abstract: Today we can observe a plethora of adaptive video stream- ing services and media players which support interoperable formats like DASH and HLS. Most of the players and their rate adaptation algorithms work as a black box. We have de- veloped a system for easy and rapid testing of media players under various network scenarios. In this paper, we introduce AdViSE, the Adaptive Video Streaming Evaluation frame- work for the automated testing of adaptive media players. The presented framework is used for the comparison and testing of media players in the context of adaptive video streaming over HTTP in web/HTML5 environments. The demonstration showcases a series of experiments with different media players under given context conditions (e.g., network shaping, delivery format). We will also demonstrate the real-time capabilities of the framework and offline anal- ysis including several QoE metrics with respect to a newly introduced bandwidth index.
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