9,633 research outputs found
Use of content analysis tools for visual interaction design
No full textAutomatic media content analysis in multimedia is a very promising field of research bringing in various possibilities for enhancing visual informatics. By computationally analysing the quantitative data contained in text, audio, image and video media, more semantically meaningful and useful information on the media contents can be derived, extracted and visualised, informing human users those facts and patterns initially hidden in the bit streams of data. Insights into how to transform the emerging technological possibilities from these media analysis tools into usable visual interfaces to help people see visual information in novel ways will be an important contribution to visual informatics. In this paper, we outline some of the more promising content analysis techniques currently being researched in multimedia and computer vision and discuss how these could be used to develop visually-oriented end-user interfaces that support searching, browsing and summarization of the media contents in various usage contexts. We illustrate this with a few example applications that we have developed over the years, all of which designed in such a way as to take advantage of the automatic content analysis and to discover and create novel usage scenarios of consuming visually-oriented media contents
Alan F. Smeaton
No full textIn this paper we describe work done as part of the TREC-4 benchmarking exercise by a team from Dublin City University. In TREC-4 we had 3 activities as follows: . In work on improving the efficiency of standard SMART-like query processing we have applied various thresholding processes to the postings list of an inverted file and we have limited the number of document score accumulators available during query processing. The first run we submitted for evaluation in TREC-4 (DCU951) used our best set of thresholding and accumulator set parameters. . The second run we submitted is based upon a query expansion using terms from WordNet. Essentially, for each original query term we determine its level of specificity or abstraction; for broad terms we add more specific terms, for specific original terms we add broader ones; for ones in-between we add both broader and narrower terms. When the query is expanded we then delete all the original query terms in order to add to the judged pool, doc..
sj-docx-1-heb-10.1177_10901981231163609 – Supplemental material for Teaching Health Literacy and Digital Literacy Together at University Level: The FLOURISH Module
No full textSupplemental material, sj-docx-1-heb-10.1177_10901981231163609 for Teaching Health Literacy and Digital Literacy Together at University Level: The FLOURISH Module by Alan F. Smeaton in Health Education & Behavior</p
Summarisation & Visualisation of Large Volumes of Time-Series Sensor Data
No full texta number of sensors, including an electricity usage
sensor supplied by Episensor. This poses our second
With the increasing ubiquity of sensor data, challenge, how to summarise an extended period of
presenting this data in a meaningful way to electrictiy usage data for a home user.
users is a challenge that must be addressed
before we can easily deploy real-world sensor
network interfaces in the home or workplace. In
this paper, we will present one solution to the
visualisation of large quantities of sensor data
that is easy to understand and yet provides
meaningful and intuitive information to a user,
even when examining many weeks or months of
historical data. We will illustrate this
visulalisation technique with two real-world
deployments of sensing the person and sensing
the home
Fergus Kelledy Alan F. Smeaton
No full textIn this paper we describe work done as part of the TREC-5 benchmarking exercise by a team from Dublin City University. In TREC-5 we had three activities as follows: . Our ad hoc submissions employ Query Space Reduction techniques which attempt to minimise the amount of data processed by an IR search engine during the retrieval process. We submitted four runs for evaluation, two automatic and two manual with one automatic run and one manual run employing our Query Space Reduction techniques. The paper reports our findings in terms of retrieval effectiveness and also in terms of the savings we make in execution time. . Our submission to the multi-lingual track (Spanish) in TREC-5 involves evaluating the performance of a new stemming algorithm for Spanish developed by Martin Porter. We submitted three runs for evaluation, two automatic, and one manual, involving a manual expansion from retrieved documents. . Character shape coding (CSC) is a technique for representing scanned text usin..
Managing personal information
No full textThere is an increasing awareness of the potential that our own self-gathered personal information has for our wellness and our health. This is partly because of our increasing awareness of what others -- the major internet companies mainly -- have been able to do with the personal information that they gather about us. The biggest hurdle to us using and usefully exploiting our own self-gathered personal data are the applications to support that.
In this paper we highlight both the potential and the challenges associated with more widespread use of our own personal data by ourselves and we point at ways in which we believe this might happen. We use the work done in lifelogging and the annual Lifelog Search Challenge as an indicator of what we can do with our own data. We review the small number of existing systems which do allow aggregation of our own personal information and show how the use of large language models could make the management of our personal data more straightforward
Computer vision for supporting image search
No full textComputer vision and multimedia information processing have made extreme progress within the last decade and many tasks can be done with a level of accuracy as if done by humans, or better. This is because we leverage the benefits of huge amounts of data available for training, we have enormous computer processing available and we have seen the evolution of machine learning as a suite of techniques to process data and deliver accurate vision-based systems. What kind of applications do we use this processing for? We use this in autonomous vehicle navigation or in security applications, searching CCTV for example, and in medical image analysis for healthcare diagnostics. One application which is not widespread is image or video search directly by users. In this paper we present the need for such image finding or re-finding by examining human memory and when it fails, thus motivating the need for a different approach to image search which is outlined, along with the requirements of computer vision to support it
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