# The Application of Fuzzy Theory in the Evaluation of Visual Images of Smartphone Rear Cameras
## 3 Results and Discussions
### Results of the Images Adjective Extraction and Factor Analysis  
Using the questionnaire method with expert participants, the image adjectives were sorted. A total of 17 experts were invited to take part in the experiment, including two secondary school Chinese language teachers, 10 postgraduates and 5 doctoral candidates majoring in design. Those participants were asked to select, from 150 adjectives, and with reference to the 2D diagrams of the representative samples (selected via the multi-scaling method), 40-50 adjectives that matched best with the visual images of the rear cameras of the smartphones. Lastly, based on mean selected times, the 40 most recognized terms were selected for further analysis (see Table 5). The 40 most recognized adjectives were combined with the 11 representative samples for the images questionnaire. The results of the questionnaire were input into SPSS (a statistical software package) for analysis. Then through T test, 34 distinctive adjectives were sorted for the first factor analysis. After component analysis, 30 factors that contained load capacity values greater than 0.6 were chosen for the second factor analysis. The outcome is shown in Table 6.  
The KMO, after factor analysis, together with the Bartlett testing result, indicates that the KMO value was 0.931, showing that the analysis material was appropriate. [PERSON]'s spherical testing value was 3191.715 (df = 435, \(p\) = 0.000), reaching significance, which represents that the relative matrix in the origin cluster contained common factors. In contrast, in Table 7, the total variance denotes five factors with values greater than 1, and shows a total variance quantity of 70.866 percent. From the transformed matrix, as shown in Table 8, five components of the last factor analysis are extremely distinct from each other, without coinciding with other factors. Therefore, the 30 adjectives and five components from this factor analysis were able to be further used. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Adjectives** & **Initial** & **Extraction** & **Adjectives** & **Initial** & **Extraction** \\ \hline Concise & 1.000 & 0.703 & Simple & 1.000 & 0.731 \\ Fascinating & 1.000 & 0.745 & Quality & 1.000 & 0.755 \\ Superior & 1.000 & 0.743 & Unique & 1.000 & 0.692 \\ Premium & 1.000 & 0.678 & Fashionable & 1.000 & 0.659 \\ Valuable & 1.000 & 0.635 & Rigid & 1.000 & 0.634 \\ Mechanical & 1.000 & 0.622 & Spotlit & 1.000 & 0.728 \\ Ordered & 1.000 & 0.727 & Sequential & 1.000 & 0.720 \\ Durable & 1.000 & 0.752 & Harmonious & 1.000 & 0.744 \\ Future & 1.000 & 0.674 & Convenient & 1.000 & 0.723 \\ Technical & 1.000 & 0.666 & Solid & 1.000 & 0.724 \\ Groundbreaking & 1.000 & 0.750 & Innovative & 1.000 & 0.756 \\ Balanced & 1.000 & 0.628 & Original & 1.000 & 0.742 \\ Uniform & 1.000 & 0.742 & Creative & 1.000 & 0.725 \\ Newfangled & 1.000 & 0.742 & Rational & 1.000 & 0.686 \\ Individual & 1.000 & 0.726 & Classic & 1.000 & 0.706 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Thirty adjective factors with absolute load capacity values greater than 0.6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline Scientific & Quality & Modern & Precise & Concise \\ Fascinating & Practical & Superior & Premium & Valuable \\ Advanced & Mechanical & Smart & Dense & Ordered \\ Durable & Future & Technical & Simple & Quality \\ Unique & Detailed & Fashionable & Rigid & Spotlit \\ Sequential & Harmonious & Convenient & Exquisite & Solid \\ Groundbreaking & Innovative & Balanced & Original & Uniform \\ Creative & Newfangled & Rational & Individual & Classic \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The 40 adjectives that were most recognized. In this research, it was necessary to acquire adjectives that are appropriate for evaluating the visual images of smartphone rear cameras. Therefore, using factor analysis, from 150 adjectives, 30 image adjective clusters were created, consisting of five factors.