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Published September 25, 2016 | Version v1
Conference paper Open

VIDEO AESTHETIC QUALITY ASSESSMENT USING KERNEL SUPPORT VECTOR MACHINE WITH ISOTROPIC GAUSSIAN SAMPLE UNCERTAINTY (KSVM-IGSU)

  • 1. Information Technologies Institute (ITI), CERTH, Queen Mary University of London
  • 2. Information Technologies Institute (ITI), CERTH
  • 3. Queen Mary University of London

Description

In this paper we propose a video aesthetic quality assessment method that combines the representation of each video according to a set of photographic and cinematographic rules, with the use of a learning method that takes the video representation's uncertainty into consideration. Specifically, our method exploits the information derived from both low- and high-level analysis of video layout, leading to a photo- and motion-based video representation scheme. Subsequently, a kernel Support Vector Machine (SVM) extension, the KSVM-iGSU, is trained to classify the videos and retrieve those of high aesthetic value. Experimental results on our large dataset verify the effectiveness of the proposed method. We also make publicly available our dataset, in order to facilitate research in the area of video aesthetic quality assessment.

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Additional details

Funding

MOVING – Training towards a society of data-savvy information professionals to enable open leadership innovation 693092
European Commission
InVID – In Video Veritas – Verification of Social Media Video Content for the News Industry 687786
European Commission