Video similarity detection using fixed-length Statistical Dominant Colour Profile (SDCP) signatures

S. Bekhet, A. Ahmed

Research output: Contribution to journalArticle (journal)peer-review

1 Citation (Scopus)


This paper presents a fast and effective technique for videos’ visual similarity detection and measurement using compact fixed-length signatures. The proposed technique facilitates for building real-time and scalable video matching/retrieval systems through generating a representative signature for a given video shot. The generated signature (Statistical Dominant Colour Profile, SDCP) effectively encodes the colours’ spatio-temporal patterns in a given shot, towards a robust real-time matching. Furthermore, the SDCP signature is engineered to better address the visual similarity problem, through its relaxed representation of shot contents. The compact fixed-length aspect of the proposed signature is the key to its high matching speed (>1000 fps) compared to the current techniques that relies on exhaustive processing, such as dense trajectories. The SDCP signature encodes a given video shot with only 294 values, regardless of the shot length, which facilitates for speedy signature extraction and matching. To maximize the benefit of the proposed technique, compressed-domain videos are utilized as a case study following their wide availability. However, the proposed technique avoids full video decompression and operates on tiny frames, rather than full-size decompressed frames. This is achievable through using the tiny DC-images sequence of the MPEG compressed stream. The experiments on various standard and challenging datasets (e.g. UCF101 13k videos) shows the technique’s robust performance, in terms of both, retrieval ability and computational performances.
Original languageEnglish
Pages (from-to)1999-2014
JournalJournal of Real-Time Image Processing
Issue number6
Publication statusPublished - 1 Dec 2019

Research Centres

  • Centre for Intelligent Visual Computing Research


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