4,302 research outputs found
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Challenges and opportunities of context-aware information access
Ubiquitous computing environments embedding a wide range of pervasive computing technologies provide a challenging and exciting new domain for information access. Individuals working in these environments are increasingly permanently connected to rich information resources. An appealing opportunity of these environments is the potential to deliver useful information to individuals either from their previous information experiences or external sources. This information should enrich their life experiences or make them more effective in their endeavours. Information access in ubiquitous computing environments can be made "context-aware" by exploiting the wide range context data available describing the environment, the searcher and the information itself. Realizing such a vision of reliable, timely and appropriate identification and delivery of information in this way poses numerous challenges. A central theme in achieving context-aware information access is the combination of information retrieval with multiple dimensions of available context data. Potential context data sources, include the user's current task, inputs from environmental and biometric sensors, associated with the user's current context, previous contexts, and document context, which can be exploited using a variety of technologies to create new and exciting possibilities for information access
Gazo bunseki to kanren joho o riyoshita gazo imi rikai ni kansuru kenkyu
制度:新 ; 報告番号:甲3514号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2012/2/8 ; 早大学位記番号:新585
Photo2Video: Semantic-Aware Deep Learning-Based Video Generation from Still Content
Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses essentially textual content or the identification and selection of specific types of content. A wealth of possibilities are yet to be explored by bringing the use of ML into the multimedia creative process, allowing the knowledge inferred by the former to influence automatically how new multimedia content is created. The work presented in this article provides contributions in three distinct ways towards this goal: firstly, it proposes a methodology to re-train popular neural network models in identifying new thematic concepts in static visual content and attaching meaningful annotations to the detected regions of interest; secondly, it presents varied visual digital effects and corresponding tools that can be automatically called upon to apply such effects in a previously analyzed photo; thirdly, it defines a complete automated creative workflow, from the acquisition of a photograph and corresponding contextual data, through the ML region-based annotation, to the automatic application of digital effects and generation of a semantically aware multimedia story driven by the previously derived situational and visual contextual data. Additionally, it presents a variant of this automated workflow by offering to the user the possibility of manipulating the automatic annotations in an assisted manner. The final aim is to transform a static digital photo into a short video clip, taking into account the information acquired. The final result strongly contrasts with current standard approaches of creating random movements, by implementing an intelligent content- and context-aware video.The work presented in this paper has been supported by the European Commission under
contract number H2020-ICT-20-2017-1-RIA-780612 and by National Funds through the Portuguese
funding agency, FCT—Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020.info:eu-repo/semantics/publishedVersio
Ranking News-Quality Multimedia
News editors need to find the photos that best illustrate a news piece and
fulfill news-media quality standards, while being pressed to also find the most
recent photos of live events. Recently, it became common to use social-media
content in the context of news media for its unique value in terms of immediacy
and quality. Consequently, the amount of images to be considered and filtered
through is now too much to be handled by a person. To aid the news editor in
this process, we propose a framework designed to deliver high-quality,
news-press type photos to the user. The framework, composed of two parts, is
based on a ranking algorithm tuned to rank professional media highly and a
visual SPAM detection module designed to filter-out low-quality media. The core
ranking algorithm is leveraged by aesthetic, social and deep-learning semantic
features. Evaluation showed that the proposed framework is effective at finding
high-quality photos (true-positive rate) achieving a retrieval MAP of 64.5% and
a classification precision of 70%.Comment: To appear in ICMR'1
DEArt: Dataset of European Art
Large datasets that were made publicly available to the research community
over the last 20 years have been a key enabling factor for the advances in deep
learning algorithms for NLP or computer vision. These datasets are generally
pairs of aligned image / manually annotated metadata, where images are
photographs of everyday life. Scholarly and historical content, on the other
hand, treat subjects that are not necessarily popular to a general audience,
they may not always contain a large number of data points, and new data may be
difficult or impossible to collect. Some exceptions do exist, for instance,
scientific or health data, but this is not the case for cultural heritage (CH).
The poor performance of the best models in computer vision - when tested over
artworks - coupled with the lack of extensively annotated datasets for CH, and
the fact that artwork images depict objects and actions not captured by
photographs, indicate that a CH-specific dataset would be highly valuable for
this community. We propose DEArt, at this point primarily an object detection
and pose classification dataset meant to be a reference for paintings between
the XIIth and the XVIIIth centuries. It contains more than 15000 images, about
80% non-iconic, aligned with manual annotations for the bounding boxes
identifying all instances of 69 classes as well as 12 possible poses for boxes
identifying human-like objects. Of these, more than 50 classes are CH-specific
and thus do not appear in other datasets; these reflect imaginary beings,
symbolic entities and other categories related to art. Additionally, existing
datasets do not include pose annotations. Our results show that object
detectors for the cultural heritage domain can achieve a level of precision
comparable to state-of-art models for generic images via transfer learning.Comment: VISART VI. Workshop at the European Conference of Computer Vision
(ECCV
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