Members of DCAPI have presented and showed their research work in the Annual Showcase Event for the School of Computer Science, University of Lincoln. (14th and 15th May). Saddam also won the “Best Demo” prize for his video matching & retrieval interactive demo.
Saddam receiving his “Certificate of Achievment” for “Best Demo”.
Saddam presenting his research work in Video Matching and retrieval.
Postgraduates by Research (PGRs) had all day on Wed 14th May and featured in the morning of Thursday 15th May as well, with visitors and companies representatives.
Saddam demonstrating his “Interactive, drag-n-drop Video matching and retrieval” demo to visitors & companies representatives and colleaguesAll around the poster, with explanation from Saddam on his work on the “Compressed Video matching and retrieval”
The event is organised by Dr Amr Ahmed (Leader of the DCAPI group, and the Program Leader for PGRs), for a number of years.
Annual “Showcase Event” for School of Computer Science. Dr Amr Ahmed take this intitiative and organised this event for a number of years.Amr is arranging the Registration and welcome table.
The event was also officially opened (and concluded) by the Head of School, Dr David Cobham, who attended the full program and handed the certificates to winners as well as the Helpers, including the Admin team.
Head of School, Dr David Cobham (left)Head of School handing in the “Thank You” Certificates for Helpers and the Admin Team.Head of School handing in the “Thank You” Certificates for Helpers and the Admin Team.
All had fun during the Poster session and inbetween the sessions as well.
PGRs together, with the Head of School and the Program Leader for PGRs, following the Posters session
The paper “Compact Signature-based Compressed Video Matching Using Dominant Colour Profiles (DCP)” has been accepted in the ICPR 2014 conference http://www.icpr2014.org/, and will be presented in August 2014, Stockholm, Sweden.
Abstract— This paper presents a novel technique for efficient and generic matching of compressed video shots, through compact signatures extracted directly without decompression. The compact signature is based on the Dominant Color Profile (DCP); a sequence of dominant colors extracted and arranged as a sequence of spikes in analogy to the human retinal representation of a scene. The proposed signature represents a given video shot with ~490 integer values, facilitating for real-time processing to retrieve a maximum set of matching videos. The technique is able to work directly on MPEG compressed videos, without full decompression, as it utilizes the DC-image as a base for extracting color features. The DC-image has a highly reduced size, while retaining most of visual aspects, and provides high performance compared to the full I-frame. The experiments and results on various standard datasets show the promising performance, both the accuracy and the efficient computation complexity, of the proposed technique.
Clinical reports includes valuable medical-related information in free-form text which can be extremely useful in aiding/providing better patient care. Text analysis techniques have demonstrated the potential to unlock such information from text. I2b2* designed a smoking challenge requiring the automatic classification of patients in relation to smoking status, based on clinical reports (Uzuner Ö et al,2008) . This was motivated by the benefits that such classification and similar extractions can be useful in further studies/research, e.g. asthma studies.
Aim & Motivation
Our aim is to investigate the potential of achieving similar results by analysing the increasing and widely available/accessible online user-generated contents (UGC), e.g. forums. This is motivated by the fact that clinical reports are not widely available and has a long and rigorous process to approve any access.
We also aimed at investigating appropriate compact feature sets that facilitate further level of studies; e.g. Psycholinguistics, as explained later.
Methodology
•Data collected, systematically and with set criteria, from web forums.
•Some properties of the text, for forum data and clinical reports, were extracted to compare the writing style in clinical and forum (shown to the left and below).
•Machine learning (Support Vector Machine) classifier model was built from the collected data, using a baseline feature sets (as per the I2B2 challenge), for each data set (clinical and forum)
•Another model was built using a new feature set LIWC (Linguistic Inquiry and Word Count) + POS (Part of Speech) , for each data set (clinical and forum).
•Smoking status classification accuracy was calculated for each of the above models on each dataset.
Results
•In general, the classification accuracy from forum posts is found to be in line with the baseline results done on clinical records (figure 1).
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•Using LIWC+POS features (125 feature) for classification obtained slightly less accuracy, compared to baseline features (>20K feature), but the feature set is compact and facilitates further levels of studies (Psycholinguistic)
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•Different factors that affect classification accuracy of forum posts, with LIWC+POS, have been explored, such as (figure 2):
o Post’s length (number of words).
o Data set size (number of posts).
o Removing parts of the features .
Conclusion & Future work
The results suggest that analysing user-generated contents, such as forums, can be as useful as clinical reports. The proposed LIWC+POS feature set, while achieving comparable results, is highly compact and facilitates further levels of studies (e.g. Psycholinguistics).
We expect our work to be for health researchers, medical industrial, by providing them with tools to quantify and better understand people smoking relation and how they behave online, and for forum members, by enriching their use of this rapidly developing and increasingly popular medium by searching for peoples who are in the same situation.
For future work:
• Improve the classification accuracy, with LIWC+POS, and use this feature set as a tool for further and deeper analysis of a person’s emotion and psychological status at various stages of the stop smoking process (in journey to stop smoking).
•Integrating other lexical dictionary such as WordNet to capture more colloquiall words and expressions which are not included in LIWC dictionary.
Reference:
Uzuner Ö, Goldstein I, Kohane I. Identifying patient smoking status from medical discharge records. J Am Med Inform. 2008;15(1):14-24. PMID:17947624.
