5,784 research outputs found
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and âenablersâ, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Developing a comprehensive framework for multimodal feature extraction
Feature extraction is a critical component of many applied data science
workflows. In recent years, rapid advances in artificial intelligence and
machine learning have led to an explosion of feature extraction tools and
services that allow data scientists to cheaply and effectively annotate their
data along a vast array of dimensions---ranging from detecting faces in images
to analyzing the sentiment expressed in coherent text. Unfortunately, the
proliferation of powerful feature extraction services has been mirrored by a
corresponding expansion in the number of distinct interfaces to feature
extraction services. In a world where nearly every new service has its own API,
documentation, and/or client library, data scientists who need to combine
diverse features obtained from multiple sources are often forced to write and
maintain ever more elaborate feature extraction pipelines. To address this
challenge, we introduce a new open-source framework for comprehensive
multimodal feature extraction. Pliers is an open-source Python package that
supports standardized annotation of diverse data types (video, images, audio,
and text), and is expressly with both ease-of-use and extensibility in mind.
Users can apply a wide range of pre-existing feature extraction tools to their
data in just a few lines of Python code, and can also easily add their own
custom extractors by writing modular classes. A graph-based API enables rapid
development of complex feature extraction pipelines that output results in a
single, standardized format. We describe the package's architecture, detail its
major advantages over previous feature extraction toolboxes, and use a sample
application to a large functional MRI dataset to illustrate how pliers can
significantly reduce the time and effort required to construct sophisticated
feature extraction workflows while increasing code clarity and maintainability
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Metadata and ontologies for organizing studentsâ memories and learning: standards and convergence models for context awareness
Este artĂculo trata de las ontologĂas que sirven para la comprensiĂłn en contexto y la GestiĂłn de la InformaciĂłn Personal (PIM)y su aplicabilidad al proyecto Memex Metadata(M2). M2 es un proyecto de investigaciĂłn de la Universidad de Carolina del Norte en Chapel Hill para mejorar la memoria digital de los alumnos utilizando tablet PC, la tecnologĂa SenseCam de Microsoft y otras tecnologĂas mĂłviles(p.ej. un dispositivo de GPS) para capturar el contexto del aprendizaje. Este artĂculo presenta el proyecto M2, dicute el concepto de los portafolios digitales en las actuales tendencias educativas, relacionĂĄndolos con las tecnologĂas emergentes, revisa las ontologĂas relevantes y su relaciĂłn con el proyecto CAF (Context Awareness Framework), y concluye identificando las lĂneas de investigaciĂłn futuras.This paper focuses on ontologies supporting context awareness and Personal Information Management (PIM) and their
applicability in Memex Metadata (M2) project. M2 is a research project of the University of North Carolina at Chapel Hill to
improve student digital memories using the tablet PC, Microsoftâs SenseCam technology, and other mobile technologies (e.g.,
a GPS device) to capture context. The M2 project offers new opportunities studying studentsâ learning with digital
technologies. This paper introduces the M2 project; discusses E-portfolios and current educational trends related to pervasive
computing; reviews relevant ontologies and their relationship to the projectsâ CAF (context awareness framework), and
concludes by identifying future research directions
Multimedia information technology and the annotation of video
The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning
ArchiveSpark: Efficient Web Archive Access, Extraction and Derivation
Web archives are a valuable resource for researchers of various disciplines.
However, to use them as a scholarly source, researchers require a tool that
provides efficient access to Web archive data for extraction and derivation of
smaller datasets. Besides efficient access we identify five other objectives
based on practical researcher needs such as ease of use, extensibility and
reusability.
Towards these objectives we propose ArchiveSpark, a framework for efficient,
distributed Web archive processing that builds a research corpus by working on
existing and standardized data formats commonly held by Web archiving
institutions. Performance optimizations in ArchiveSpark, facilitated by the use
of a widely available metadata index, result in significant speed-ups of data
processing. Our benchmarks show that ArchiveSpark is faster than alternative
approaches without depending on any additional data stores while improving
usability by seamlessly integrating queries and derivations with external
tools.Comment: JCDL 2016, Newark, NJ, US
Access to recorded interviews: A research agenda
Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed
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Linking Data Across Universities: An Integrated Video Lectures Dataset
This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scraped and structured according to well-known vocabularies, like FOAF 1, or the W3C Ontology for Media Resources 2. To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project 3. An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14,000 video lectures from 27 different institutions and categorized under a common classification scheme
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