This article models multicolor perceptual features to investigate human perceptual responses to multiple colors based on a subjective evaluation experiment. First, an image set containing 164 multicolor samples was constructed, and multicolor objective features containing two categories with 60 dimensions were designed and quantified. Based on this, the 164 samples were evaluated by 30 subjects based on nine perceptual descriptive variables, including “swell‐shrink,” “light‐dark,” and “far‐near.” Then, a mathematical model of multicolor perceptual features (nine descriptive variables) was established by support vector regression. Finally, this article conducted model evaluation and factor analysis, and the results showed that the model could predict multicolor perceptual features well. It was also observed that, unlike single‐color perception, space perception was produced when applying multiple colors.
Skeleton-based human action recognition based on Neural Architecture Search (NAS.) adopts a one-shot NAS strategy. It improves the speed of evaluating candidate models in the search space through weight sharing, which has attracted significant attention. However, directly applying the one-shot NAS method for skeleton recognition requires training a super-net with a large search space that traverses various combinations of model parameters, which often leads to overly large network models and high computational costs. In addition, when training this super-net, the one-shot NAS needs to traverse the entire search space of the complete skeleton recognition task. Furthermore, the traditional method does not consider the optimization of the search strategy. As a result, a significant amount of search time is required to obtain a better skeleton recognition network model. A more efficient weighting model, a NAS skeleton recognition model based on the Single Path One-shot (SNAS-GCN) strategy, is proposed to address the above challenges. First, to reduce the model search space, a simplified four-category search space is introduced to replace the mainstream multi-category search space. Second, to improve the model search efficiency, a single-path one-shot approach is introduced, through which the model randomly samples one architecture at each step of the search training optimization. Finally, an adaptive Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is proposed to obtain a candidate structure of the perfect model automatically. With these three steps, the entire network architecture of the recognition model (and its weights) is fully and equally trained significantly. The search and training costs will be greatly reduced. The search-out model is trained by the NTU-RGB + D and Kinetics datasets to evaluate the performance of the proposed model’s search strategy. The experimental results show that the search time of the proposed method in this paper is 0.3 times longer than that of the state-of-the-art method. Meanwhile, the recognition accuracy is roughly comparable compared to that of the SOTA NAS-GCN method.
Color harmony is the hotspot of many researchers in the fields of art and design which is widely used in various artworks and design activities. As for psychology, color harmony is closely related to human's perception and cognition, although, the research of its generation process has not been fully focused on. With experimental psychology with artificial intelligence technology combined together, our aim is to investigate the factors affecting color harmony on the basis of the generation process from perception to cognition, and construct a mathematical model to predict its degree, so as to realize the quantitatively analysis and measurement of color harmony. We classify the factors affecting color harmony into objective and subjective ones. Among them, the objective factors are constructed based on the three principle elements of color with a total of 21-dimensional color-pair objective physical features extracted. And the subjective factors are divided into direct psychological effects and indirect psychological effects. 180 two-color combinations were evaluated by 30 subjects based on color harmony and its subjective factors on discrete scales ranging from -2 to 2. Correlation analysis reveals that color harmony is not only affected by objective factors, but also by subjective factors which has a stronger correlation than that of the objective ones. Finally, machine learning algorithms were adopted to construct a mathematical model, which has been proved that the linear relationship can well explain the generation mechanism of color harmony.
Skeleton-based action recognition is a research hotspot in the field of computer vision. Currently, the mainstream method is based on Graph Convolutional Networks (GCNs). Although there are many advantages of GCNs, GCNs mainly rely on graph topologies to draw dependencies between the joints, which are limited in capturing long-distance dependencies. Meanwhile, Transformer-based methods have been applied to skeleton-based action recognition because they effectively capture long-distance dependencies. However, existing Transformer-based methods lose the inherent connection information of human skeleton joints because they do not yet focus on initial graph structure information. This paper aims to improve the accuracy of skeleton-based action recognition. Therefore, a Graph Skeleton Transformer network (GSTN) for action recognition is proposed, which is based on Transformer architecture to extract global features, while using undirected graph information represented by the symmetric matrix to extract local features. Two encodings are utilized in feature processing to improve joints’ semantic and centrality features. In the process of multi-stream fusion strategies, a grid-search-based method is used to assign weights to each input stream to optimize the fusion results. We tested our method using three action recognition datasets: NTU RGB+D 60, NTU RGB+D 120, and NW-UCLA. The experimental results show that our model’s accuracy is comparable to state-of-the-art approaches.
This article constructs the hierarchical model of multicolor‐emotion generation network (MCEG‐Net) to investigate human emotional responses to multiple colors based on the research of multicolor perceptual features modeling, which reveals the generation mechanism of multicolor emotion from the perspective of hierarchy of visual features. First, the 164 multicolor samples were evaluated by 30 subjects based on PAD emotion space. On this basis, the correlation analysis was carried out among color basic attributes, multicolor physical features, multicolor perceptual features and emotion. Then, a mathematical model of multicolor emotion was constructed by using multiple linear regression (MLR) algorithm. It was observed that, the multiple colors was strongly correlated with emotion. Finally, the MCEG‐Net was constructed, which reveals the generation mechanism of the process the “the basic attributes of color stimulate human's perceptual reflex and then generate emotion” under the interaction of multiple colors from the perspective of information science. The results showed that the mathematical model could predict color emotion well. In addition, the MCEG‐Net provides a new way to reveal the generation mechanism of color‐emotion associations.
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