The paper (titled “Video Matching Using DC-image and Local Features”) was presented by Saddam Bekhet (PhD Rsearcher) in the International Conference of Signal and Image Engineering (ICSIE’13), during the World Congress on Engineering 2013, in London UK.
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.
New Conference paper accepted for publishing in “World Congress on Engineering 2013“.
The paper title is “Video Matching Using DC-image and Local Features ”
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.
(Click Semantic Video Annotation-with Knowledge ” https://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.
