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Medical Imaging 2005 Conference

作者： Antani, S Natarajan, M Long, JL Long, LR Thoma, GR NIH Lister Hill Natl Ctr Biomed Commun Natl Lib Med DHHS Bethesda MD 20894 USA

ISBN: (纸本)0819457221

The article describes the status of our ongoing R&D at the U.S. National Library of Medicine (NLM) towards the development of an advanced multimedia database biomedical information system that supports content-based image retrieval (CBIR). NLM maintains a collection of 17,000 digitized spinal X-rays along with text survey data from the Second National Health and Nutritional Examination Survey (NHANES ii). These data serve as a rich data source for epidemiologists and researchers of osteoarthritis and musculoskeletal diseases. It is currently possible to access these through text keyword queries using our Web-based Medical Information Retrieval System (WebMIRS). CBIR methods developed specifically for biomedical images could offer direct visual searching of these images by means of example image or user sketch. We are building a system which supports hybrid queries that have text and image-content components. R&D goals include developing algorithms for robust image segmentation for localizing and identifying relevant anatomy, labeling the segmented anatomy based on its pathology, developing suitable indexing and similarity matching methods for images and image features, and associating the survey text information for query and retrieval along with the image data. Some highlights of the system developed in MATLAB and Java are: use of a networked or local centralized database for text and image data;flexibility to incorporate new research work;provides a means to control access to system components under development;and use of XML for structured reporting. The article details the design, features, and algorithms in this third revision of this prototype system, CBIR3.

关键词： content-based image retrieval (CBIR) NHANES ii anterior osteophytes medical image databases medical image informatics image segmentation shape similarity procrustes distance Fourier descriptors polygon approximation

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DOAS: Device-Oriented Adaptive multimedia Scheme for 3GPP LTE systems

DOAS: Device-Oriented Adaptive Multimedia Scheme for 3GPP LT...

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IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Zou, Longhao Trestian, Ramona Muntean, Gabriel-Miro Dublin City Univ Sch Elect Engn Performance Engn Lab Dublin 9 Ireland

ISBN: (纸本)9781467362351

The growing popularity of the high-end mobile computing devices-smartphones, tablets, notebooks and more equipped with high-speed network access, enables the mobile user to watch multimedia content from any source on any screen, at any time, while on the move or stationary. In this context, the network operators must ensure smooth video streaming with the lowest service delay, jitter, and packet loss. This paper proposes a resource efficient Device-Oriented Adaptive multimedia Scheme (DOAS) built on top of the downlink scheduler in LTE-Advanced systems. DOAS bases its adaptation decision on the end-user device display resolution information and Quality of Service (QoS). DOAS is implemented on top of the Proportional Fair (PF) and the well-known Modified Largest Weighted Delay First (M-LWDF) scheduling algorithms within the 3GPP LTE/LTE-Advanced system. The performance of the proposed adaptive multimedia scheme was analyzed and compared against a non-adaptive solution in terms of throughput, packet loss and PSNR.

关键词： Long-term Evolution Mobile Device Resolution Downlink Scheduling

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RL-Belady: A Unified Learning Framework for content Caching 20

RL-Belady: A Unified Learning Framework for Content Caching

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28th ACM International Conference on multimedia (MM)

作者： Yan, Gang Li, Jian SUNY Binghamton Binghamton NY 13902 USA

ISBN: (纸本)9781450379885

content streaming is the dominant application in today's Internet, which is typically distributed via content delivery networks (CDNs). CDNs usually use caching as a means to reduce user access latency so as to enable faster content downloads. Typical analysis of caching systems either focuses on content admission, which decides whether to cache a content, or content eviction to decide which content to evict when the cache is full. This paper instead proposes a novel framework that can simultaneously learn both content admission and content eviction for caching in CDNs. To attain this goal, we first put forward a lightweight architecture for content next request time prediction. We then leverage reinforcement learning (RL) along with the prediction to learn the time-varying content popularities for content admission, and develop a simple threshold-based model for content eviction. We call this new algorithm RL-Belady (RLB). In addition, we address several key challenges to design learning-based caching algorithms, including how to guarantee lightweight training and prediction with both content eviction and admission in consideration, limit memory overhead, reduce randomness and improve robustness in RL stochastic optimization. Our evaluation results using 3 production CDN datasets show that RLB can consistently outperform state-of-the-art methods with dramatically reduced running time and modest overhead.

关键词： Cache Admission Cache Eviction content Delivery Networks Machine Learning Belady

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Joint multimedia fingerprinting and encryption: Security issues and some solutions

Joint multimedia fingerprinting and encryption: Security iss...

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IEEE International Conference on multimedia and Expo (ICME 2007)

作者： Sun, Shih-Wei Lu, Chun-Shien Chang, Pao-Chi Acad Sinica Inst Informat Sci Taipei 115 Taiwan Natl Chung Yuan Univ Dept Elect Engn Chungli 320 Taiwan Natl Chung Yuan Univ Dept Commun Engn Chungli 320 Taiwan

ISBN: (纸本)9781424410163

In this paper, a new multimedia joint fingerprinting and encryption (JFE) scheme embedded into the advanced access content system (AACS) is proposed. Like other security-related systems, there exist some security threats to the proposed framework To cope with these difficulties, the contributions of this paper include: (i) we apply multimedia encryption at different points to resist some attacks points,and (ii) we propose rewritable fingerprint embedding (RFE) to deal with some multi-point collusion attacks. Experimental results are provided to demonstrate the proposed AACS-compatible JFE method.

关键词： Network security

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SATIN: a persistent musical database for music information retrieval and a supporting deep learning experiment on song instrumental classification

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multimedia TOOLS AND APPLICATIONS 2019年第3期78卷 2703-2718页

作者： Bayle, Yann Robine, Matthias Hanna, Pierre Univ Bordeaux LaBRI UMR 5800 F-33400 Talence France CNRS LaBRI UMR 5800 F-33400 Talence France

This paper introduces SATIN, the Set of Audio Tags and Identifiers Normalized. SATIN is a database of 400k audio-related metadata and identifiers that aims at facilitating reproducibility and comparisons among the Music Information Retrieval (MIR) algorithms. The idea is to take advantage of partnerships between scientists and private companies that host millions of tracks. Scientists can send their feature extraction algorithm to companies along SATIN identifiers and retrieve the corresponding features. This procedure allows the MIR community to have access to more tracks for classification purposes. Afterwards, scientists can provide to the MIR community the classification result for each track, which can then be compared with other algorithms results. SATIN thus resolves the major problems of accessing more tracks, managing copyrights locks, saving computation time, and guaranteeing consistency over research databases. We introduce SOFT1, the first Set Of FeaTures extracted by a company thanks to SATIN. We propose a supporting experiment classifying instrumentals and songs to detail a possible use of SATIN. We compare a deep learning approach that has emerged in recent years in MIR with a knowledge-based approach.

关键词： Acoustic signal processing Classification of instrumentals and songs content-based audio retrieval Database Machine learning algorithms Music information retrieval Music recommendation Playlist generation Reproducibility Signal analysis Signal processing algorithms Music autotagging

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TRACE: Linguistic-based Approach for Automatic Lecture Video Segmentation Leveraging Wikipedia Texts 17

TRACE: Linguistic-based Approach for Automatic Lecture Video...

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IEEE International Symposium on multimedia (ISM)

作者： Shah, Rajiv Ratn Yu, Yi Shaikh, Anwar Dilawar Zimmermann, Roger Natl Univ Singapore Sch Comp Singapore Singapore Natl Inst Informat Digital Content & Media Sci Res Div Tokyo Japan Delhi Technol Univ Dept Comp Engn Delhi India NUSRI Suzhou Peoples R China

ISBN: (纸本)9781509003792

In multimedia-based e-learning systems, the accessibility and searchability of most lecture video content is still insufficient due to the unscripted and spontaneous speech of the speakers. Moreover, this problem becomes even more challenging when the quality of such lecture videos is not sufficiently high. To extract the structural knowledge of a multi-topic lecture video and thus make it easily accessible it is very desirable to divide each video into shorter clips by performing an automatic topic-wise video segmentation. To this end, this paper presents the TRACE system to automatically perform such a segmentation based on a linguistic approach using Wikipedia texts. TRACE has two main contributions: (i) the extraction of a novel linguistic-based Wikipedia feature to segment lecture videos efficiently, and (ii) the investigation of the late fusion of video segmentation results derived from state-of-the-art algorithms. Specifically for the late fusion, we combine confidence scores produced by the models constructed from visual, transcriptional, and Wikipedia features. According to our experiments on lecture videos from VideoLectures. NET and NPTEL1, the proposed algorithm segments knowledge structures more accurately compared to existing state-of-the-art algorithms. The evaluation results are very encouraging and thus confirm the effectiveness of TRACE.

关键词： Lecture video segmentation e-learning systems multimodal analysis Wikipedia late fusion linguistic features

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A hybrid metaheuristic approach to optimize the content transmission in multimedia systems 11

A hybrid metaheuristic approach to optimize the content tran...

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11th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2014

作者： Gómez, Arthur T. Nesi, Luan C. Martins, Marcio G. Chiwiacowsky, Leonardo D. Master Program on Applied Computing University of the Sinos Valley Av. Unisinos 950 São Leopoldo/RS Brazil

ISBN: (纸本)9789897580390

The advent of the digital television in Brazil has allowed users to access interactive channels. Once interactive channels are available, the users are able to find multimedia content such as movies and breaking news programs, to send and/or receive emails, to access interactive applications and also other contents. In this context, a high demand of requests from users is expected. Therefore, from the content provider's point of view, the determination of transmission parameters is needed in order to ensure the best quality of transmission to every user. The aforementioned identification problem is modelled as an optimization problem and a solution procedure based on metaheuristic techniques is proposed. Genetic Algorithm and Tabu Search metaheuristics are employed separately and coupled in a hybrid scheme to define the best transmission policy, optimizing the transmission parameters, such as audio and video transmission rates. Based on the experimental results, the hybrid algorithm has produced better solutions which meet the quality requirements.

关键词： Genetic algorithms

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A model to support sign language content development for digital television

A model to support sign language content development for dig...

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2009 IEEE International Workshop on multimedia Signal Processing, MMSP '09

作者： De Brito, Ronnie Fagundes Pereira, Alice T. Cybis Departamento de Engenharia e Gestão do Conhecimento Universidade Federal de Santa Catarina Florianópolis Brazil

ISBN: (纸本)9781424444649

This paper proposes a model to support sign language content development and deployment in digital television scenarios. The model objective is to overcome traditional video limitations on the production of signed content and improve accessibility on digital television. Previous work on sign and gesture recognition, sign notation systems and avatar animation are reviewed. Sign communication requirements are investigated and adequate algorithms are identified to enable sign language processing and transmission. An application interface is proposed in order to access the model functionalities. The final discussion lists the benefits and limitations of the proposed model. © 2009 IEEE.

关键词： Digital television

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content-based Retrieval in multimedia Databases Based on Feature Models

Content-based Retrieval in Multimedia Databases Based on Fea...

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作者： ApersP.M.G. KerstenM.L. University of British Columbia

With the increasing popularity of WWW, the main challenge in computer science has become content-based retrieval of multimedia objects. Until now access of multimedia objects in databases was done by means of keywords. Now, with the integration of feature-detection algorithms in database systems software, content-based retrieval can be fully integrated with query processing. In this paper, we describe our experimentation platform under development that fully integrates traditional query processing and content-based retrieval and that is based on feature databases, making database technology available to multimedia.

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Optimizing Perceptual Quality Prediction Models for multimedia Processing systems: Going Beyond the Mean Opinion Score

Optimizing Perceptual Quality Prediction Models for Multimed...

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作者： FOTIO TIOTSOP, LOHIC Polytechnic University of Turin

学位级别：博士

Being able to accurately predict human perceived quality of multimedia con- tent (e. g., images and videos) through computer algorithms is an important and hot research topic, since it directly leads to several advantages: storage capacity and bandwidth savings for large organizations, and possibility to offer higher qual- ity services when the communication channel capacity is limited. This PhD thesis focuses on using machine learning algorithms and advanced statistical methods to find new ways of addressing the problem. Human viewers can easily distinguish between an image with a pleasant per- ceptual quality and another one whose quality is corrupted by artifacts introduced for instance by a codec. This task that is straightforward for a human due to his daily experiences and background, is absolutely not easy for an algorithm. For this reason, in the literature, human opinion scores on the perceptual quality of a multimedia content are considered as ground truth data. Any quality prediction algorithm (QPA) is then designed to predict the so-called Mean Opinion Score (MOS) of a multimedia content, i. e., the average of the scores that a group of hu- man viewers would express if they were asked to watch that content and rate its perceptual quality on a given numerical scale. Several QPAs aiming at MOS prediction have been proposed in the literature but the state-of-the-art in media quality assessment is still suffering some crucial limitations. Some of these limitations can be summarized as follows: i) none of the existing QPAs is accurate in all application scenarios, and thus there is still large room for their improvement; ii) QPAs, as estimators of the MOS, do not provide a complete measure of quality-of-experience (QoE). In fact, QPAs simply measure the quality perception of an "average viewer" and only marginally account for the individual expectation and uncertainty that characterize different users of the same service. For instance, QPAs do not provide ans

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