6,838 research outputs found
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
Investigating the use of semantic technologies in spatial mapping applications
Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend
A Semantic Graph-Based Approach for Mining Common Topics From Multiple Asynchronous Text Streams
In the age of Web 2.0, a substantial amount of unstructured
content are distributed through multiple text streams in an
asynchronous fashion, which makes it increasingly difficult
to glean and distill useful information. An effective way to
explore the information in text streams is topic modelling,
which can further facilitate other applications such as search,
information browsing, and pattern mining. In this paper, we
propose a semantic graph based topic modelling approach
for structuring asynchronous text streams. Our model in-
tegrates topic mining and time synchronization, two core
modules for addressing the problem, into a unified model.
Specifically, for handling the lexical gap issues, we use global
semantic graphs of each timestamp for capturing the hid-
den interaction among entities from all the text streams.
For dealing with the sources asynchronism problem, local
semantic graphs are employed to discover similar topics of
different entities that can be potentially separated by time
gaps. Our experiment on two real-world datasets shows that
the proposed model significantly outperforms the existing
ones
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Linking early geospatial documents, one place at a time: annotation of geographic documents with Recogito
Recogito is an open source tool for the semi-automatic annotation of place references in maps and texts. It was developed as part of the Pelagios 3 research project, which aims to build up a comprehensive directory of places referred to in early maps and geographic writing predating the year 1492. Pelagios 3 focuses specifically on sources from the Classical Latin, Greek and Byzantine periods; on Mappae Mundi and narrative texts from the European Medieval period; on Late Medieval Portolans; and on maps and texts from the early Islamic and early Chinese traditions. Since the start of the project in September 2013, the team has harvested more than 120,000 toponyms, manually verifying almost 60,000 of them. Furthermore, the team held two public annotation workshops supported through the Open Humanities Awards 2014. In these workshops, a mixed audience of students and academics of different backgrounds used Recogito to add several thousand contributions on each workshop day.
A number of benefits arise out of this work: on the one hand, the digital identification of places – and the names used for them – makes the documents' contents amenable to information retrieval technology, i.e. documents become more easily search- and discoverable to users than through conventional metadata-based search alone. On the other hand, the documents are opened up to new forms of re-use. For example, it becomes possible to “map” and compare the narrative of texts, and the contents of maps with modern day tools like Web maps and GIS; or to analyze and contrast documents’ geographic properties, toponymy and spatial relationships. Seen in a wider context, we argue that initiatives such as ours contribute to the growing ecosystem of the “Graph of Humanities Data” that is gathering pace in the Digital Humanities (linking data about people, places, events, canonical references, etc.), which has the potential to open up new avenues for computational and quantitative research in a variety of fields including History, Geography, Archaeology, Classics, Genealogy and Modern Languages
A brief network analysis of Artificial Intelligence publication
In this paper, we present an illustration to the history of Artificial
Intelligence(AI) with a statistical analysis of publish since 1940. We
collected and mined through the IEEE publish data base to analysis the
geological and chronological variance of the activeness of research in AI. The
connections between different institutes are showed. The result shows that the
leading community of AI research are mainly in the USA, China, the Europe and
Japan. The key institutes, authors and the research hotspots are revealed. It
is found that the research institutes in the fields like Data Mining, Computer
Vision, Pattern Recognition and some other fields of Machine Learning are quite
consistent, implying a strong interaction between the community of each field.
It is also showed that the research of Electronic Engineering and Industrial or
Commercial applications are very active in California. Japan is also publishing
a lot of papers in robotics. Due to the limitation of data source, the result
might be overly influenced by the number of published articles, which is to our
best improved by applying network keynode analysis on the research community
instead of merely count the number of publish.Comment: 18 pages, 7 figure
An Evaluation of Popular Copy-Move Forgery Detection Approaches
A copy-move forgery is created by copying and pasting content within the same
image, and potentially post-processing it. In recent years, the detection of
copy-move forgeries has become one of the most actively researched topics in
blind image forensics. A considerable number of different algorithms have been
proposed focusing on different types of postprocessed copies. In this paper, we
aim to answer which copy-move forgery detection algorithms and processing steps
(e.g., matching, filtering, outlier detection, affine transformation
estimation) perform best in various postprocessing scenarios. The focus of our
analysis is to evaluate the performance of previously proposed feature sets. We
achieve this by casting existing algorithms in a common pipeline. In this
paper, we examined the 15 most prominent feature sets. We analyzed the
detection performance on a per-image basis and on a per-pixel basis. We created
a challenging real-world copy-move dataset, and a software framework for
systematic image manipulation. Experiments show, that the keypoint-based
features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and
Zernike features perform very well. These feature sets exhibit the best
robustness against various noise sources and downsampling, while reliably
identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper
appeared in IEEE Transaction on Information Forensics and Securit
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