11,062 research outputs found
On the automated interpretation and indexing of American football
This work combines natural language understanding and image processing with incremental learning to develop a system that can automatically interpret and index American Football. We have developed a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain. Our representation combines expert knowledge, domain knowledge, spatial knowledge and temporal knowledge. We also present an incremental learning algorithm to improve the knowledge base as well as to keep previously developed concepts consistent with new data. The advantages of the incremental learning algorithm are that is that it does not split concepts and it generates a compact conceptual hierarchy which does not store instances
COSMOS-7: Video-oriented MPEG-7 scheme for modelling and filtering of semantic content
MPEG-7 prescribes a format for semantic content models for multimedia to ensure interoperability across a multitude of platforms and application domains. However, the standard leaves it open as to how the models should be used and how their content should be filtered. Filtering is a technique used to retrieve only content relevant to user requirements, thereby reducing the necessary content-sifting effort of the user. This paper proposes an MPEG-7 scheme that can be deployed for semantic content modelling and filtering of digital video. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user
Simulated evaluation of faceted browsing based on feature selection
In this paper we explore the limitations of facet based browsing which uses sub-needs of an information need for querying and organising the search process in video retrieval. The underlying assumption of this approach is that the search effectiveness will be enhanced if such an approach is employed for interactive video retrieval using textual and visual features. We explore the performance bounds of a faceted system by carrying out a simulated user evaluation on TRECVid data sets, and also on the logs of a prior user experiment with the system. We first present a methodology to reduce the dimensionality of features by selecting the most important ones. Then, we discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. Facets created by users are simulated by clustering video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness
A Formal Framework for Linguistic Annotation
`Linguistic annotation' covers any descriptive or analytic notations applied
to raw language data. The basic data may be in the form of time functions --
audio, video and/or physiological recordings -- or it may be textual. The added
notations may include transcriptions of all sorts (from phonetic features to
discourse structures), part-of-speech and sense tagging, syntactic analysis,
`named entity' identification, co-reference annotation, and so on. While there
are several ongoing efforts to provide formats and tools for such annotations
and to publish annotated linguistic databases, the lack of widely accepted
standards is becoming a critical problem. Proposed standards, to the extent
they exist, have focussed on file formats. This paper focuses instead on the
logical structure of linguistic annotations. We survey a wide variety of
existing annotation formats and demonstrate a common conceptual core, the
annotation graph. This provides a formal framework for constructing,
maintaining and searching linguistic annotations, while remaining consistent
with many alternative data structures and file formats.Comment: 49 page
Oblivion: Mitigating Privacy Leaks by Controlling the Discoverability of Online Information
Search engines are the prevalently used tools to collect information about
individuals on the Internet. Search results typically comprise a variety of
sources that contain personal information -- either intentionally released by
the person herself, or unintentionally leaked or published by third parties,
often with detrimental effects on the individual's privacy. To grant
individuals the ability to regain control over their disseminated personal
information, the European Court of Justice recently ruled that EU citizens have
a right to be forgotten in the sense that indexing systems, must offer them
technical means to request removal of links from search results that point to
sources violating their data protection rights. As of now, these technical
means consist of a web form that requires a user to manually identify all
relevant links upfront and to insert them into the web form, followed by a
manual evaluation by employees of the indexing system to assess if the request
is eligible and lawful.
We propose a universal framework Oblivion to support the automation of the
right to be forgotten in a scalable, provable and privacy-preserving manner.
First, Oblivion enables a user to automatically find and tag her disseminated
personal information using natural language processing and image recognition
techniques and file a request in a privacy-preserving manner. Second, Oblivion
provides indexing systems with an automated and provable eligibility mechanism,
asserting that the author of a request is indeed affected by an online
resource. The automated ligibility proof ensures censorship-resistance so that
only legitimately affected individuals can request the removal of corresponding
links from search results. We have conducted comprehensive evaluations, showing
that Oblivion is capable of handling 278 removal requests per second, and is
hence suitable for large-scale deployment
Video Data Visualization System: Semantic Classification And Personalization
We present in this paper an intelligent video data visualization tool, based
on semantic classification, for retrieving and exploring a large scale corpus
of videos. Our work is based on semantic classification resulting from semantic
analysis of video. The obtained classes will be projected in the visualization
space. The graph is represented by nodes and edges, the nodes are the keyframes
of video documents and the edges are the relation between documents and the
classes of documents. Finally, we construct the user's profile, based on the
interaction with the system, to render the system more adequate to its
references.Comment: graphic
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
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