Patent ID: 11967115
Assignee: WUHAN TEXTILE UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G  A  Y | IPC A  G

Claim 0:
1. A color matching evaluation method combining similarity measure and visual perception, wherein the method comprises the following steps:
Step 1, acquiring sample images, and constructing a to-be-tested sample image database;
Step 2, improving a K-Means algorithm by using a silhouette coefficient method, and extracting main colors of the sample images to obtain a subject color palette P1 corresponding to each image;
Step 3, generating corresponding palettes P2 using an intelligent color recommendation system according to the main colors of the samples; and
Step 4, if each palette is a combination of n color blocks, generating auxiliary palettes using P1 and P2, calculating minimum color differences of the paired palettes through a color difference model, and converting the minimum color differences into similarity measures as an evaluation indicator I;
an implementation of generating an auxiliary palette in Step 4 is as follows:
Step 1, marking an original palette as P1, marking a generated palette as P2, calculating color differences respectively from the n colors of P2 to the first color of P1, and taking the color corresponding to a minimum color difference as the first color of a new palette;
Step2, calculating color differences respectively from the n colors of P2 to the second, third, and nth colors of P1, determining the second, third, and nth colors of the new palette according to the method described above, that is, generating a new auxiliary palette again using the generated palette P2, and marking the new auxiliary palette as P3;
Step3, calculating an average color difference between the original palette P1 and the palette P3, wherein an average color difference calculation method comprises: respectively calculating color differences between the corresponding colors of the two palettes, that is, between the ith color of the palette P1 and the ith color of the palette P3, and a calculation formula is shown in Formula XI, wherein i, n, and Ci represent the ith pair of colors, a total number of the colors of the palettes, and the average color difference of the corresponding ith colors, and the minimum color difference from the palette P1 to the palette P2 is marked as m1;, m
       1
      
      =
      
       
        
         
          ∑
          n
         
         
          i
          =
          1
         
        
        
         C
         i
        
       
       n
      
     
     ;
    
   
   
    
     Formula
     ⁢
        
     VI
    
   
  
 

Step4, repeating the operation of Step2, generating a new auxiliary palette for P2 using the n colors of P1, and marking the new palette as P4; and
Step5, calculating an average color difference between the generated palette P2 and the palette P4, and marking the minimum color difference from P2 to P1 as m2;
a specific implementation of converting the minimum color differences into similarity measures as an evaluation indicator I in Step 4 is as follows:
firstly, calculating the minimum color difference between the palette P1 and the palette P2;

m=(m1+m2)/2  Formula VII; and

then, subtracting a normalized maximum theoretical value from a color-difference-normalized result to obtain a similarity measure between the palettes, as shown in Formula XII,, M
      =
      
       
        m
        -
        m_min
       
       
        m_max
        -
        m_min
       
      
     
     ;
    
   
   
    
     Formula
     ⁢
        
     XI
    
   
  
 

 
  
   
    
     
      S
      =
      
       1
       -
       M
      
     
     ;
    
   
   
    
     Formula
     ⁢
        
     XII
    
   
  
 

wherein m, m_min, and m_max respectively represents the minimum color difference between the palette P1 and the palette P2 and the minimum color difference and maximum color difference of all the palettes, and M and S represent the normalized result and the similarity measure between the palettes;
Step 5, performing color transfer on the source sample images using the generated palettes P2 to obtain corresponding paired images;
Step 6, calculating structural similarities between the paired images, and marking results as an evaluation indicator II;
Step 7, conducting an eye movement tracking experiment on the source sample images and sample images subjected to the color transfer to obtain eye movement data between the paired images, converting the eye movement data into a visual perception measure, and marking the visual perception measure as an evaluation indicator III;
Step 8, assigning different weights to the evaluation indicator I and the evaluation indicator II to construct an image-content-based evaluation system; and
Step 9, calculating Pearson correlation coefficients for the image-content-based evaluation system corresponding to the different weights and the evaluation indicator III in Step 7, and obtaining an optimal evaluation indicator corresponding to an optimal weight.