publication . Other literature type . Conference object . 2016

Video aesthetic quality assessment using kernel Support Vector Machine with isotropic Gaussian sample uncertainty (KSVM-IGSU)

Vasileios Mezaris; Christos Tzelepis; Ioannis Patras; Eftichia Mavridaki;
Open Access
  • Published: 25 Sep 2016
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
<p>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 ...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Uncategorized, zenodo, Video aesthetic quality assessment, Rules of photography and cinematography, Video representation uncertainty, Kernel (linear algebra), Support vector machine, Video tracking, Exploit, Video quality, Gaussian, symbols.namesake, symbols, Kernel method, Artificial intelligence, business.industry, business, Computer vision, Computer science, Feature extraction
Funded by
EC| InVID
Project
InVID
In Video Veritas – Verification of Social Media Video Content for the News Industry
  • Funder: European Commission (EC)
  • Project Code: 687786
  • Funding stream: H2020 | IA
,
EC| MOVING
Project
MOVING
Training towards a society of data-savvy information professionals to enable open leadership innovation
  • Funder: European Commission (EC)
  • Project Code: 693092
  • Funding stream: H2020 | RIA
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Conference object . 2016
Provider: FigShare
ZENODO
Conference object . 2016
Provider: ZENODO
ZENODO
Conference object . 2016
Provider: ZENODO
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