PhD Studentship – “Object and Action Recognition”

Summary:  “Object and Action Recognition Assisted by Computational Linguistics”.

The aim of this project is to investigate how computer vision methods such as object and
action recognition may be assisted by computational linguistic models, such as WordNet.
The main challenge of object and action recognition is the scalability of methods from
dealing with a dozen of categories (e.g. PASCAL VOC) to thousands of concepts (e.g.
ImageNet ILSVRC). This project is expected to contribute to the application of automated
visual content annotation and more widely to bridging the semantic gap between
computational approaches of vision and language.

Deadline: 20th March 2015.

A PhD studentship is advertised at

Object and Action Recognition Assisted by Computational Linguistics

The research is collaboration between University of Kingston (Digital Imaging Research Centre) and University of Lincoln (DCAPI group)

For details of the applications process: http://www.kingston.ac.uk/research/research-degrees/funding/phd-studentships-2015/faqs/

Deadline: 20th March 2015

Conference paper presented in “ ICPR 2014”

Saddam Bekhet presented his accepted paper in  ICPR2014 , Stockholm, Sweden
The paper title is “Compact Signature-based Compressed Video Matching Using Dominant Colour Profiles (DCP)

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.

Congratulations and well done for Saddam and Amr.

DSC_0402  DSC_0409   DSC_0393   DSC_0283

 

PGRs Showcase Event

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 receiving his “Certificate of Achievment” for “Best Demo”.

 

 

 

 

 

 

 

 

 

Saddam presenting his research work in Video Matching and retrieval.

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 colleagues

Saddam demonstrating his “Interactive, drag-n-drop Video matching and retrieval” demo to visitors & companies representatives and colleagues

All around the poster, with explanation from Saddam on his work on the "Compressed Video matching and retrieval"

All 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.

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.

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, 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.

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

PGRs together, with the Head of School and the Program Leader for PGRs, following the Posters session

 

 

 

 

 

 

 

 

 

 

 

New paper accepted in ICPR 2014 – “Compact Signature-based Compressed Video Matching Using Dominant Colour Profiles (DCP)”

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.

Congratulations and well done for Saddam.

Text Analysis for Health Related Applications, from UGC

Text Analysis of User-Generated Contents for Health Related Applications

Deema Abdal Hafeth, Amr Ahmed, David Cobham

Downloads:   Paper (PDF) ,   Dataset 

Poster - Download PDF from the link below

Poster – Download PDF from the link below

Text Analysis for Health Applications_Poster

 

Introduction

 

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).
•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)
•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.

 

 

Poster presented at university of Lincoln postgraduate conference

Saddam Bekhet presented a poster at university of Lincoln postgraduate conference 2014.

20140403_13051620140403_130716

 

New Chapter titled “DC-Image for Real Time Compressed Video Matching” is published in Springer Transactions on Engineering Technologies

New chapter titled “DC-Image for Real Time Compressed Video Matching” is published in Springer Transactions on Engineering Technologies 2014 ,

Well done and congratulations to Saddam Bekhet

Interested in joining us as a “Research Fellow”?
Get in touch by emailing aahmed@lincoln.ac.uk

Welcome to the DCAPI research group.

Our multi-disciplinary research is mainly focused on the analysis and mining of digital contents (Visual; images and videos, and textual). This includes Computer Vision, Image/Video Processing and analysis, Semantic Analysis, annotation, Action recognition, Image/Video Matching and similarity (Copy & Near-Duplicate detection), and many others.

We welcome any discussion and potential collaboration. Please get in touch with us (contacts on the right side-bar).

MPhil Transfer

Congratulations to Saddam Bekhet , he passed his MPhil transfer and he is now officially a PhD researcher , wish Saddam all the Luck and success in his life.

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