Deep Learning for motion based video aesthetics

Aesthetics is defined by the properties of arts and beauty, thus making it a very subjective domain. In our day to day lives, with the increase of multimedia requirements, the aesthetic appeal of images and videos has gained much importance in varied fields like advertising, film making, User-Interface design, social networking etc. Visual attributes greatly affect the aesthetic sense of the viewers. In this paper, to start with, we dive into the details of low level, middle level and high level image attributes that contribute towards the aesthetic appeal of images. Videos share their attributes with images except for the presence of motion in a video. Next, we proceed towards the handcrafted and deep learning techniques for assessing image and video attributes for their aesthetic appeal. Motion is an important but seldom explored visual attribute that affects video aesthetic appeal. Typically, slow motion creates an impact and appreciation amongst the viewers as they absorb the contents of the video better in comparison to faster motion in the video. Surveys conducted showcased the human inclination towards slowly paced videos in comparison to the fast-paced ones. We have experimented with the deep learning framework for detecting motion in nature based videos. Deep learning achieves an impressive performance in comparison to the handcrafted methods, thus reinforcing current trust in the deep learning frameworks for multimedia analysis.