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 KSVMiGSU, 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.
In REF a video aesthetic assessment method is presented that combines a video representation integrating photographic and cinematographic rules, and a learning mechanism that takes video representation uncertainty into consideration.
Video aesthetic quality assessment using kernel Support Vector Machine with isotropic Gaussian sample uncertainty (KSVM-IGSU)
2016 IEEE International Conference on Image Processing (ICIP)
2016 IEEE International Conference on Image Processing (ICIP)
Computer Science