Dr Saddam Bekhet, successfully passed viva

Congratulations to Saddam who successfully passed his PhD VIVA on 23rd May 2016.

Examiners have commended Saddam’s work and contributions. They also emphasized how well written the thesis is.

A well-deserved achievement Saddam, well done.

And all the best for your future career.

 

“DC-Image for Real Time Compressed Video Matching” 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.

Best Student Paper Award 2013 – WCE 2013

Congratulations to Saddam Bekhet (PhD Researcher) who achieved the “Best Student Paper Award 2013″  for his conference paper entitled “Video Matching Using DC-image and Local Features ” presented earlier in “World Congress on Engineering 2013“ in London .
Award copy20130704_112542

 

 

Abstract: This paper presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without decompression. The relevant arguments and supporting evidences are discussed for developing video similarity techniques that works directly on compressed videos, without decompression, and especially utilising small size images. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and the corresponding computation complexity. The second experiment compares between using local features and global features in video matching, especially in the compressed domain and with the small size images. The results confirmed that the use of DC-image, despite its highly reduced size, is promising as it produces at least similar (if not better) matching precision, compared to the full I-frame. Also, using SIFT, as a local feature, outperforms precision of most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin. There are also various optimisations that can be done to improve this computation complexity.

 

Automatic Semantic Video Annotation

Automatic Semantic Video Annotation

Amjad Altadmri, Amr Ahmed*, Andrew Hunter

Poster - Click here to download PDF.

Poster – see link below to download PDF.

 

 

 

(Click Semantic Video Annotation-with Knowledge ” http://amrahmed.blogs.lincoln.ac.uk/files/2013/03/Semantic-Video-Annotation-with-Knowledge.pdf  ,  to download the pdf)

INTRODUCTION

The volume of video data is growing exponentially. This data need to be annotated to facilitate search and retrieval, so that we can quickly find a video whenever needed.

Manual Annotation, especially for such volume, is time consuming and would be expensive. Hence, automated annotation systems are required.

 

AIM

Automated Semantic Annotation of wide-domain videos (i.e. no domain restrictions). This is an important step towards bridging the “Semantic Gap” in video understanding.

 

METHOD

1. Extracting “Video Signature” for each  video.
2. Match signatures to find most similar  videos, with annotations
3. Analyse and process obtained annotations, in consultation with Common-sense knowledge-bases
4. Produce the suggested annotation.

EVALUATION

• Two standard, and challenging  Datasets  were used. TRECVID BBC Rush and UCF.
• Black-box and White-box testing carried out.
•Measures include: Precision, Confusion Matrix.

CONCLUSION

•Developed an Automatic Semantic Video Annotation framework.
•Not restricted to a specific domain videos.
•Utilising Common-sense Knowledge enhances scene understanding and improve semantic annotation.
Publications
  1. A framework for automatic semantic video annotation 
    Altadmri, Amjad and Ahmed, Amr (2013) A framework for automatic semantic video annotation. Multimedia Tools and Applications, 64 (2). ISSN 1380-7501.
  2. Semantic levels of domain-independent commonsense knowledgebase for visual indexing and retrieval applications 
    Altadmri, Amjad and Ahmed, Amr and Mohtasseb Billah, Haytham (2012) Semantic levels of domain-independent commonsense knowledgebase for visual indexing and retrieval applications. Neural Information Processing. Lecture Notes in Computer Science, 7663 . pp. 640-647. ISSN 0302-9743
  3. VisualNet: commonsense knowledgebase for video and image indexing and retrieval application 
    Alabdullah Altadmri, Amjad and Ahmed, Amr (2009) VisualNet: commonsense knowledgebase for video and image indexing and retrieval application. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, 21-22 November 2009, Shanghai, China..
  4. Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases 
    Altadmri, Amjad and Ahmed, Amr (2009) Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases. In: The IEEE International Conference on Signal and Image Processing Applications (ICSIPA 2009), 18-19th November 2009, Malaysia.
  5. Video databases annotation enhancing using commonsense knowledgebases for indexing and retrieval 
    Altadmri, Amjad and Ahmed, Amr (2009) Video databases annotation enhancing using commonsense knowledgebases for indexing and retrieval. In: The 13th IASTED International Conference on Artificial Intelligence and Soft Computing., September 7 � 9, 2009, Palma de Mallorca, Spain.