31,103 research outputs found
TagBook: A Semantic Video Representation without Supervision for Event Detection
We consider the problem of event detection in video for scenarios where only
few, or even zero examples are available for training. For this challenging
setting, the prevailing solutions in the literature rely on a semantic video
representation obtained from thousands of pre-trained concept detectors.
Different from existing work, we propose a new semantic video representation
that is based on freely available social tagged videos only, without the need
for training any intermediate concept detectors. We introduce a simple
algorithm that propagates tags from a video's nearest neighbors, similar in
spirit to the ones used for image retrieval, but redesign it for video event
detection by including video source set refinement and varying the video tag
assignment. We call our approach TagBook and study its construction,
descriptiveness and detection performance on the TRECVID 2013 and 2014
multimedia event detection datasets and the Columbia Consumer Video dataset.
Despite its simple nature, the proposed TagBook video representation is
remarkably effective for few-example and zero-example event detection, even
outperforming very recent state-of-the-art alternatives building on supervised
representations.Comment: accepted for publication as a regular paper in the IEEE Transactions
on Multimedi
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
Automatic Prediction Of Small Group Performance In Information Sharing Tasks
In this paper, we describe a novel approach, based on Markov jump processes,
to model small group conversational dynamics and to predict small group
performance. More precisely, we estimate conversational events such as turn
taking, backchannels, turn-transitions at the micro-level (1 minute windows)
and then we bridge the micro-level behavior and the macro-level performance. We
tested our approach with a cooperative task, the Information Sharing task, and
we verified the relevance of micro- level interaction dynamics in determining a
good group performance (e.g. higher speaking turns rate and more balanced
participation among group members).Comment: Presented at Collective Intelligence conference, 2012
(arXiv:1204.2991
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