21,030 research outputs found
From media crossing to media mining
This paper reviews how the concept of Media Crossing has contributed to the advancement of the application domain of information access and explores directions for a future research agenda. These will include themes that could help to broaden the scope and to incorporate the concept of medium-crossing in a more general approach that not only uses combinations of medium-specific processing, but that also exploits more abstract medium-independent representations, partly based on the foundational work on statistical language models for information retrieval. Three examples of successful applications of media crossing will be presented, with a focus on the aspects that could be considered a first step towards a generalized form of media mining
A literature review of expert problem solving using analogy
We consider software project cost estimation from a problem solving perspective. Taking a cognitive psychological approach, we argue that the algorithmic basis for CBR tools is not representative of human problem solving and this mismatch could account for inconsistent results. We describe the fundamentals of problem solving, focusing on experts solving ill-defined problems. This is supplemented by a systematic literature review of empirical studies of expert problem solving of non-trivial problems. We identified twelve studies. These studies suggest that analogical reasoning plays an important role in problem solving, but that CBR tools do not model this in a biologically plausible way. For example, the ability to induce structure and therefore find deeper analogies is widely seen as the hallmark of an expert. However, CBR tools fail to provide support for this type of reasoning for prediction. We conclude this mismatch between experts’ cognitive processes and software tools contributes to the erratic performance of analogy-based prediction
Living Knowledge
Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following
Generic 3D Representation via Pose Estimation and Matching
Though a large body of computer vision research has investigated developing
generic semantic representations, efforts towards developing a similar
representation for 3D has been limited. In this paper, we learn a generic 3D
representation through solving a set of foundational proxy 3D tasks:
object-centric camera pose estimation and wide baseline feature matching. Our
method is based upon the premise that by providing supervision over a set of
carefully selected foundational tasks, generalization to novel tasks and
abstraction capabilities can be achieved. We empirically show that the internal
representation of a multi-task ConvNet trained to solve the above core problems
generalizes to novel 3D tasks (e.g., scene layout estimation, object pose
estimation, surface normal estimation) without the need for fine-tuning and
shows traits of abstraction abilities (e.g., cross-modality pose estimation).
In the context of the core supervised tasks, we demonstrate our representation
achieves state-of-the-art wide baseline feature matching results without
requiring apriori rectification (unlike SIFT and the majority of learned
features). We also show 6DOF camera pose estimation given a pair local image
patches. The accuracy of both supervised tasks come comparable to humans.
Finally, we contribute a large-scale dataset composed of object-centric street
view scenes along with point correspondences and camera pose information, and
conclude with a discussion on the learned representation and open research
questions.Comment: Published in ECCV16. See the project website
http://3drepresentation.stanford.edu/ and dataset website
https://github.com/amir32002/3D_Street_Vie
CHORUS Deliverable 4.5: Report of the 3rd CHORUS Conference
The third and last CHORUS conference on Multimedia Search Engines took place from the 26th to the 27th of May 2009 in Brussels, Belgium. About 100 participants from 15 European countries, the US, Japan and Australia learned about the latest developments in the domain. An exhibition of 13 stands presented 16 research projects currently ongoing around the
world
Museum Experience Design: A Modern Storytelling Methodology
In this paper we propose a new direction for design, in the context of the theme “Next Digital Technologies in Arts and Culture”, by employing modern methods based on Interaction Design, Interactive Storytelling and Artificial Intelligence. Focusing on Cultural Heritage, we propose a new paradigm for Museum Experience Design, facilitating on the one hand traditional visual and multimedia communication and, on the other, a new type of interaction with artefacts, in the form of a Storytelling Experience. Museums are increasingly being transformed into hybrid spaces, where virtual (digital) information coexists with tangible artefacts. In this context, “Next Digital Technologies” play a new role, providing methods to increase cultural accessibility and enhance experience. Not only is the goal to convey stories hidden inside artefacts, as well as items or objects connected to them, but it is also to pave the way for the creation of new ones through an interactive museum experience that continues after the museum visit ends. Social sharing, in particular, can greatly increase the value of dissemination
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval
We present a simple but powerful reinterpretation of kernelized
locality-sensitive hashing (KLSH), a general and popular method developed in
the vision community for performing approximate nearest-neighbor searches in an
arbitrary reproducing kernel Hilbert space (RKHS). Our new perspective is based
on viewing the steps of the KLSH algorithm in an appropriately projected space,
and has several key theoretical and practical benefits. First, it eliminates
the problematic conceptual difficulties that are present in the existing
motivation of KLSH. Second, it yields the first formal retrieval performance
bounds for KLSH. Third, our analysis reveals two techniques for boosting the
empirical performance of KLSH. We evaluate these extensions on several
large-scale benchmark image retrieval data sets, and show that our analysis
leads to improved recall performance of at least 12%, and sometimes much
higher, over the standard KLSH method.Comment: 15 page
Case-based analysis in user requirements modelling for knowledge construction
Context: Learning can be regarded as knowledge construction in which prior knowledge and experience
serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction
has to take place continuously in order to enhance the learners’ competence in a competitive working
environment. As the information consumers, the individual users demand personalised information provision
which meets their own specific purposes, goals, and expectations.
Objectives: The current methods in requirements engineering are capable of modelling the common
user’s behaviour in the domain of knowledge construction. The users’ requirements can be represented
as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis
needs to be enhanced so that personalised information provision can be tackled and modelled. However,
there is a lack of suitable modelling methods to achieve this end. This paper presents a new
ontological method for capturing individual user’s requirements and transforming the requirements onto
personalised information provision specifications. Hence the right information can be provided to the
right user for the right purpose.
Method: An experiment was conducted based on the qualitative method. A medium size of group of users
participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content.
The results were used as the feedback for the improvement.
Result: The research work has produced an ontology model with a set of techniques which support the
functions for profiling user’s requirements, reasoning requirements patterns, generating workflow from
norms, and formulating information provision specifications.
Conclusion: The current requirements engineering approaches provide the methodical capability for
developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further
enhance the RE approaches for modelling the individual user’s needs and discovering the user’s
requirements
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