publication . Conference object . Other literature type . 2016

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

Tzelepis, Christos; Mavridaki, Eftichia; Mezaris, Vasileios; Patras, Ioannis;
English
  • Published: 25 Sep 2016
  • Publisher: IEEE
Abstract
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...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Video aesthetic quality assessment, Rules of photography and cinematography, Support vector machine, Video representation uncertainty, Kernel method, Video tracking, Video quality, Artificial intelligence, business.industry, business, Gaussian, symbols.namesake, symbols, Exploit, Computer science, Computer vision, Pattern recognition, Kernel (linear algebra), 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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Other literature type . 2016
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publication . Conference object . Other literature type . 2016

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

Tzelepis, Christos; Mavridaki, Eftichia; Mezaris, Vasileios; Patras, Ioannis;