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

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

doi:10.1109/icip.2016.7532791

Cited by 12 publications

(8 citation statements)

References 18 publications

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“…[36] set up an ADCCV dataset that enhanced the Telefonica dataset [37] by augmenting it with more positive examples. [38] assessed video aesthetic quality via a kernel SVM extension with the CERTH-ITI-VAQ700 dataset, including 350 high aesthetic quality videos and 350 low aesthetic quality videos. To summarize, neural networks are not extensively adopted for video aesthetic quality assessment subjected to small-scale datasets.…”

Section: Video Aesthetic Quality Assessmentmentioning

confidence: 99%

Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment

Kuang

Jin

Zhao

et al. 2020

IEEE Trans. Multimedia

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Despite the growing number of unmanned aerial vehicles (UAVs) and aerial videos, there is a paucity of studies focusing on the aesthetics of aerial videos that can provide valuable information for improving the aesthetic quality of aerial photography. In this article, we present a method of deep multimodality learning for UAV video aesthetic quality assessment. More specifically, a multistream framework is designed to exploit aesthetic attributes from multiple modalities, including spatial appearance, drone camera motion, and scene structure. A novel specially designed motion stream network is proposed for this new multistream framework. We construct a dataset with 6,000 UAV video shots captured by drone cameras. Our model can judge whether a UAV video was shot by professional photographers or amateurs together with the scene type classification. The experimental results reveal that our method outperforms the video classification methods and traditional SVM-based methods for video aesthetics. In addition, we present three application examples of UAV video grading, professional segment detection and aesthetic-based UAV path planning using the proposed method. Index Terms-Aesthetic quality assessment, aerial video aesthetic, deep multimodality learning.

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Section: Video Aesthetic Quality Assessmentmentioning

confidence: 99%

Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment

Kuang

Jin

Zhao

et al. 2020

IEEE Trans. Multimedia

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“…Other noteworthy features found were the motionratio and sharpness, difference measures between foreground and background. In [8] 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. They compiled a dataset for the task of video aesthetic prediction, which we use as a way to test our overall method.…”

Section: Related Workmentioning

confidence: 99%

“…A comprehensive video dataset for the problem of aesthetic quality assessment [8] with annotated scores for 700 (UGC) videos from YouTube, 350 videos are rated as being of high aesthetic quality and another 350 as being of low aesthetic quality.…”

Section: Certh-iti-vaq700mentioning

confidence: 99%

Semi-automatic Video Assessment System

Martins

Correia

2017

Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing

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This paper describes a system for semi-automatic quality assessment of user generated content (UGC) from large events. It uses image and video processing techniques 1 combined with a computational quality model that takes in account aesthetics and how human visual perception and attention mechanisms discriminate visual interest. We describe the approach and show that the developed system allows to sort and filter a large stream of UGC in an efficient and timely manner. CCS CONCEPTS • Computing methodologies → Image processing; • Computing methodologies → Visual content-based indexing and retrieval; • Computing methodologies → Graphics systems and interfaces; • Theory of computation → Support vector machines KEYWORDS UGC; large events; video quality assessment; aesthetics; interestingness; model of attention and perception.

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“…While previous works have focused on computational aesthetic classification of short videos (Niu & Liu, 2012;Tzelepis et al, 2016;Yang, Yeh, & Chen, 2011), "The Colors of Motions" by Charlie Clark illustrated the change in dominant colors over several feature films (Clark, 2014). Moreover, Jason Schulman's "Photographs of Films" offers novel ways of looking into the aesthetics of films as they overlap all frames from a film to obtain a single merged image (Shulman, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Despite recent efforts to build large datasets of videos, such as YouTube 8M, no video dataset for aesthetic video classification achieves a similar quantity of items as existing datasets for computational aesthetic classification of photographs (Abu-El-Haija et al, 2016). The largest aesthetic dataset of videos known to date is the recently published dataset by Tzelepis et al, which is composed of 700 short videos collected on YouTube and matched with aesthetic ratings (Tzelepis, Mavridaki, Mezaris, & Patras, 2016).…”

Section: Introductionmentioning

confidence: 99%

From Computational Aesthetic Prediction for Images to Films and Online Videos

Lemarchand

2017

AVANT

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In the last decade, creating and sharing videos online has become a mainstream movement and has led to some creators generating one personal video per day, also called daily vlogging. Although robust solutions exist to suggest photographs based on aesthetic criteria, the rising number of online videos created and watched means that such recommendation systems are required more than ever for videos. The main purpose of this paper is to transfer the skill of computational aesthetic classification of photographs to videos while developing new ways of investigating video creation. Using a dataset of photographs rated on aesthetic criteria by an internet community and recently developed feature extraction algorithms, the computational aesthetic classifier is capable of state-of-the-art photograph classification depending on aesthetic preferences learnt from people's ratings. On a test set of YouTube videos, the same system then displays satisfying aesthetic classification results that consist of an attempt to match the provided human aesthetic quality ratings. Achieving a transfer of skill from photograph to video classification, the computational classifier is used to analyze the evolution of aesthetics in feature films; this highlighted the aesthetic classifier's visual preferences and caused some interesting patterns to emerge that were related to filmmakers' decisions. Aesthetic classification makes it possible to observe the evolution of aesthetics over the careers of daily content creators thanks to their abundant and regular online video content. It can aid the investigation into the impact of aesthetics on the popularity of online videos using the available metadata about the internet audience's appreciation. This can also provide a new tool for video content creators to assess their work and assist them in the production of content of higher aesthetic quality.

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

Cited by 12 publications

References 18 publications

Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment

Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment

Semi-automatic Video Assessment System

From Computational Aesthetic Prediction for Images to Films and Online Videos

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