1,134 research outputs found
User experiments with the Eurovision cross-language image retrieval system
In this paper we present Eurovision, a text-based system for cross-language (CL) image retrieval.
The system is evaluated by multilingual users for two search tasks with the system configured in
English and five other languages. To our knowledge this is the first published set of user
experiments for CL image retrieval. We show that: (1) it is possible to create a usable multilingual
search engine using little knowledge of any language other than English, (2) categorizing images
assists the user's search, and (3) there are differences in the way users search between the proposed
search tasks. Based on the two search tasks and user feedback, we describe important aspects of
any CL image retrieval system
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 “enablers”, 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
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
A study of spatial data models and their application to selecting information from pictorial databases
People have always used visual techniques to locate information in the space
surrounding them. However with the advent of powerful computer systems and
user-friendly interfaces it has become possible to extend such techniques to stored
pictorial information. Pictorial database systems have in the past primarily used
mathematical or textual search techniques to locate specific pictures contained
within such databases. However these techniques have largely relied upon complex
combinations of numeric and textual queries in order to find the required
pictures. Such techniques restrict users of pictorial databases to expressing what is
in essence a visual query in a numeric or character based form. What is required
is the ability to express such queries in a form that more closely matches the user's
visual memory or perception of the picture required. It is suggested in this thesis
that spatial techniques of search are important and that two of the most important
attributes of a picture are the spatial positions and the spatial relationships of
objects contained within such pictures. It is further suggested that a database
management system which allows users to indicate the nature of their query by
visually placing iconic representations of objects on an interface in spatially
appropriate positions, is a feasible method by which pictures might be found from
a pictorial database. This thesis undertakes a detailed study of spatial techniques
using a combination of historical evidence, psychological conclusions and practical
examples to demonstrate that the spatial metaphor is an important concept and that
pictures can be readily found by visually specifying the spatial positions and
relationships between objects contained within them
Moody Blues: The Social Web, Tagging, and Nontextual Discovery Tools for Music
A common thread in discussions about the Next Generation Catalog is that it should incorporate features beyond the mere textual, one-way presentation of data. At the same time, traditional textual description of music materials often prohibits effective use of the catalog by specialists and nonspecialists alike. Librarians at Bowling Green State University have developed the HueTunes project to explore already established connections between music, color, and emotion, and incorporate those connections into a nontextual discovery tool that could enhance interdisciplinary as well as specialist use of the catalog
A survey on the use of relevance feedback for information access systems
Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems
A Web-based multimedia collaboratory. Empirical work studies in film archives
This report represents the latest study in the activity on Ecological Information Systems conducted in the Center for Human Machine Interaction situated at Ris National Laboratory and the University of Aarhus. The purpose of this activity is to give a description of the characteristics of work domains that will serve to outline the general context of concern to design of collaboratories. In addition, a set of preliminary implications for the design of a collaboratory are derived from the cognitive work analysis. To anticipate, further research on this approach to the design of collaboratories will show how the preceding analysis is likely to lead to a novel theoretical framework, called Ecological Collaborative Information Systems (ECIS), required for the design of collaboratories. The intention is to illustrate how the general principles of ECIS can be instantiated to develop a concrete design product: A crossdisciplinary and cross-cultural collaboratory to support customer service and professional research in archives. A web based Collaboratory Numerous valuable historic and cultural films and their sources are scattered in various national archives. Knowledge and usage of the multinational film material are severely impeded by access problems. To fully exploit the cultural film heritage internationally, a high degree of cross-disciplinary and international collaboration among professionals working with the film media is required. The Collaboratory for Annotation, Indexing and Retrieval of Digitized Historical Archive Material (Collate) is intended to foster and support collaboration on research, cultural mediation and preservation of films through a distributed multimedia repository. The collaboratory will provide webbased tools and interfac..
Formal concept matching and reinforcement learning in adaptive information retrieval
The superiority of the human brain in information retrieval (IR) tasks seems to come firstly
from its ability to read and understand the concepts, ideas or meanings central to documents, in
order to reason out the usefulness of documents to information needs, and secondly from its
ability to learn from experience and be adaptive to the environment. In this work we attempt to
incorporate these properties into the development of an IR model to improve document
retrieval. We investigate the applicability of concept lattices, which are based on the theory of
Formal Concept Analysis (FCA), to the representation of documents. This allows the use of
more elegant representation units, as opposed to keywords, in order to better capture
concepts/ideas expressed in natural language text. We also investigate the use of a
reinforcement leaming strategy to learn and improve document representations, based on the
information present in query statements and user relevance feedback. Features or concepts of
each document/query, formulated using FCA, are weighted separately with respect to the
documents they are in, and organised into separate concept lattices according to a subsumption
relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure
known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the
concepts in the lattice representation. This avoids implementation drawbacks faced by other
FCA-based approaches. Retrieval of a document for an information need is based on concept
matching between concept lattice representations of a document and a query. The learning
strategy works by making the similarity of relevant documents stronger and non-relevant
documents weaker for each query, depending on the relevance judgements of the users on
retrieved documents. Our approach is radically different to existing FCA-based approaches in
the following respects: concept formulation; weight assignment to object-attribute pairs; the
representation of each document in a separate concept lattice; and encoding concept lattices in
BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our
learning strategy makes use of relevance feedback information to enhance document
representations, thus making the document representations dynamic and adaptive to the user
interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are
presented and compared with published results. In particular, the performance of the system is
shown to improve significantly as the system learns from experience.The School of Computing,
University of Plymouth, UK
- …