Members of DCAPI have presented and showed their research work in the third Annual Showcase Event for the School of Computer Science, University of Lincoln. (6th and 7th May). The event accompanied by wide attendances from several companies (Google UK, Siemens, QinetiQ, mass, Artsgraphica, Heritage Lincolnshire, …).
Saddam also won the “Best Presentation” prize for his video similarity detection presentation.
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.
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.
The event is organised by Dr Amr Ahmed (Leader of the DCAPI group, and the Program Leader for PGRs), for a number of years.
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.
All had fun during the Poster session and inbetween the sessions as well.
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.
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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).