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Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

Peng, Hua and Hu, Jinghao and Wang, Haitao and Ren, Hui and Sun, Cong and Hu, Huosheng and Li, Jing (2021) 'Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions.' Information, 12 (3). ISSN 2078-2489

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Abstract

Imitation of human behaviors is one of the effective ways to develop artificial intelligence. Human dancers, standing in front of a mirror, always achieve autonomous aesthetics evaluation on their own dance motions, which are observed from the mirror. Meanwhile, in the visual aesthetics cognition of human brains, space and shape are two important visual elements perceived from motions. Inspired by the above facts, this paper proposes a novel mechanism of automatic aesthetics evaluation of robotic dance motions based on multiple visual feature integration. In the mechanism, a video of robotic dance motion is firstly converted into several kinds of motion history images, and then a spatial feature (ripple space coding) and shape features (Zernike moment and curvature-based Fourier descriptors) are extracted from the optimized motion history images. Based on feature integration, a homogeneous ensemble classifier, which uses three different random forests, is deployed to build a machine aesthetics model, aiming to make the machine possess human aesthetic ability. The feasibility of the proposed mechanism has been verified by simulation experiments, and the experimental results show that our ensemble classifier can achieve a high correct ratio of aesthetics evaluation of 75%. The performance of our mechanism is superior to those of the existing approaches.

Item Type: Article
Uncontrolled Keywords: robotic dance motion; machine aesthetics; visual understanding; motion history image; ensemble learning
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 19 Jul 2021 14:01
Last Modified: 19 Jul 2021 14:01
URI: http://repository.essex.ac.uk/id/eprint/30755

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