# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
## 3 Constructing NTTSSD Paradigms for Imagery Style of Industrial Product Forms
### Quantifying the Imagery Style of Industrial Product Forms Based on Kansei Engineering
#### 3.1.1 Acquisition of Cross-Category Industrial Product Forms

\end{table}
Table 6: _Cont._into high, medium, low, and zero curvature levels to improve the accuracy of product form description. Through the variations in visual curvature, product forms that are difficult to distinguish in human judgement can be distinguished more clearly, as shown in Figure 6.  
Below is the formula for calculating the visual curvature, as described by [PERSON] [71]:  
\[K_{N,\Delta S}(v)=\pi\frac{\sum_{0}^{N-1}\#[H_{ai}(s(v))]}{N\Delta S} \tag{6}\]  
Figure 5: Examples of contour lines, edge transition angles, and visual directions across six imagery styles. Figure 6: Changes in visual curvature across different product forms. where \(s(v)\) represents the neighborhood of the point with width \(\Delta S\) on the curve, and \(\#[H_{ai}(s(v))]\) represents the number of extreme points in the neighborhood \(s(v)\) of the point \(v\) on the height function \(H_{ai}\), regardless of whether the extreme point is extremely large or extremely small. Four key visual morphological features--contour line (\(l\)), edge transition angle (\(\theta\)), visual direction (\(d\)), and visual curvature (\(k\))--are used to reveal changes in the characteristics of industrial product form. Each imagery style vocabulary varies along these four dimensions. We match the six key imagery style vocabularies--Naturalness, Technology, Toughness, Steadiness, Softness, and Dynamism (NTTSSD)--with each of these four dimensions to construct an NTTSSD paradigm for product form. This ultimately results in a semantic description framework as defined below:  
\[\text{NTTSSD}=\left(N_{a},T_{e},T_{o},S_{o},S_{t},D_{y}\right) \tag{7}\]  
where the NTTSSD paradigm is composed of Naturalness, Technology, Toughness, Steadiness, Softness, and Dynamism, and each style is represented by an initial letter and a second lowercase letter, e.g., Naturalness is represented by Na for its semantic meaning. According to the above analysis, each morphological style can be represented as:  
\[Y_{i}=l_{i}\ \wedge\ \theta_{i}\ \wedge\ d_{i}\ \wedge\ k_{i},\ \ \ \ \ \ i\in\left\{N_{a},\ T_{e},T_{o},S_{o},S_{t},D_{y}\right\} \tag{8}\]  
where \(l_{i}\), \(\theta_{i}\), \(d_{i}\), _and \(k_{i}\)_ represent contour line (\(l\)), edge transition angle (\(\theta\)), visual direction (\(d\)), and visual curvature (\(k\)) of the product form. By applying this framework, designers can systematically adjust the features \(l\), \(\theta\), \(d\), and \(k\) to achieve the desired imagery style \(Y_{i}\) in alignment with user preferences. For instance, these characteristics can be defined as:  
\[Y_{Na}=\{\text{''Free curve dominated''}\}\ \wedge\{\text{''Large arc with no visible sharp corners or sharp edges''}\}\] (9) \[\In this paper, customer-perceived product form is summarized in the semantic rule sets (9) to (14). A judgement about which style a morphology belongs to is made in the order of contour line (l), edge transition angle (\(\theta\)), visual direction (d), and visual curvature (k). If the first rule can be used to make a decision, there is no need to check subsequent rules. However, the fuzzy criterion for evaluating morphological styles presents significant challenges in comprehensively quantifying them. Deep learning models after being pre-trained can automatically make the right decisions if the test cases are within the same subject of the training objects, which can improve the efficiency of identifying and understanding complex stylistic features. Therefore, this paper annotates a large-scale dataset based on the NTTSSD paradigms to extract visual forms that align with the perception of most consumers. Subsequently, deep learning models are used to learn and extract complex visual feature patterns from these data, helping designers more accurately understand and optimize product forms.