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 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 Colour Profile (DCP); a sequence of dominant colours 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 is utilizing the DC-image as a base for extracting colour 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.
Analysis and experimentation results of using DC-image, and comparisons with full image (I-Frame), can be found in Video matching using DC-image and local features (http://eprints.lincoln.ac.uk/12680/)
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.
Congratulations to Dr Amjad Altadmri for completeing his PhD degree. Amjad received his PhD degree in the formal September Graduation Ceremony at the Lincoln Cathedral.
- Amjad Graduation Ceremony – September 2013
His PhD titled “Semantic Annotation of Domain-Independent Uncontrolled Videos, Incorporating Visual Similarity and Commonsesne Knowledge Bases”. The work produced a Framework for semantic video annotation. In addition, VisualNet was also produced, which is a semantic Network for Visual-related applications.
The photo shows Dr Amjad Altadmri (Left) with his Director of Studies/Supervisor Dr Amr Ahmed ( right).
A list of publications of Amjad’s PhD are below:
- 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.
- 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
- 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..
- 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.
- 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 ï¿½ 9, 2009, Palma de Mallorca, Spain..
Amjad has also participated, with Amr and other members of the DCAPI group, in various workshops especially the V&L EPSRC Network workshops. They presented sessions and showed posters; see related blog posts:
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 .
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.