Creating a Computable Cognitive Model of Visual Aesthetics for Automatic Aesthetics Evaluation of Robotic Dance Poses

Abstract

Inspired by human dancers who can evaluate the aesthetics of their own dance poses through mirror observation, this paper presents a corresponding mechanism for robots to improve their cognitive and autonomous abilities. Essentially, the proposed mechanism is a brain-like intelligent system that is symmetrical to the visual cognitive nervous system of the human brain. Specifically, a computable cognitive model of visual aesthetics is developed using the two important aesthetic cognitive neural models of the human brain, which is then applied in the automatic aesthetics evaluation of robotic dance poses. Three kinds of features (color, shape and orientation) are extracted in a manner similar to the visual feature elements extracted by human brains. After applying machine learning methods in different feature combinations, machine aesthetics models are built for automatic evaluation of robotic dance poses. The simulation results show that our approach can process visual information effectively by cognitive computation, and achieved a very good evaluation performance of automatic aesthetics.

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

“…This also leads to the correlations between machine learning methods for aesthetic evaluation and art forms. Random forest is a suitable machine learning method for aesthetic evaluation in the field of robotic dance, which has been verified on robotic dance poses [35]. Therefore, random forest was selected as the specific implementation method of the base classifier in ensemble learning, and three random forests were formed to be a homogeneous ensemble classifier.…”

“…In the aesthetic cognition of human beings, visual information plays an important role [24]. Meanwhile, for the human brain, all visual processing determines what objects in the field of vision are and where they are located [35]. Therefore, spatial and shape features are helpful to characterize the target in the aesthetic cognition of the human brain.…”

Automatic Aesthetics Evaluation of Robotic Dance Poses Based on Hierarchical Processing Network

“…Table 3 shows the performance comparison results among these machine aesthetic models. Specifically, the comparative experiments included the following two aspects: (1) e approach in [10] was reproduced on the real dataset of robotic dance poses;…”

“…Some researchers have explored the aesthetic problems of robotic dance poses and proposed some feasible approaches. Moreover, these approaches involve only two di erent categories: human subjective aesthetics [5][6][7] and machine learning-based methods [7][8][9][10].…”

“…Based on the integrated feature, different machine learning methods are applied for training machine aesthetics models, which are further used for automatic aesthetics evaluation of robotic dance poses. In [10], a computable cognitive model of visual aesthetics was developed and applied in the automatic aesthetics evaluation of robotic dance poses. From the visual channel, three kinds of features (colour, shape, and orientation) are extracted and fused to characterize a robotic dance pose, and then machine aesthetics models are built by using different machine learning methods.…”