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

Claim 1:
2. The color matching evaluation method combining the similarity measure and the visual perception according to claim 1, wherein a specific implementation of Step 2 is as follows:, J
      =
      
       
        ∑
        
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         ∑
         
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         K
        
        
         
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           k
          
         
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             C
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          2
         
        
       
      
     
     ;
    
   
   
    
     Formula
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     I
    
   
  
 

 
  
   
    
     s
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      {
      
       
        
         
          
           
            1
            -
            
             a
             b
            
           
           ,
          
         
         
          
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           a
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       ;
      
     
    
   
   
    
     Formula
     ⁢
        
     II
    
   
  
 

wherein J represents a sum of distances between all types; n represents an index of a pixel in a background image; C(n) represents a color value of the pixel; N represents a total number of sample color data; K represents a quantity of color types; k represents a kth type of color; rnk is two-component, indicating whether a current color belongs to the kth type of color; μk represents a clustering center of the kth type of color; a represents an average distance between a sample point and all other points in the same cluster, namely, a similarity between the sample point and other points in the same cluster; b represents an average distance between the sample point and all points in the next nearest cluster, namely, a similarity between the sample point and other points in the next nearest cluster; s represents a value of a silhouette coefficient, wherein if s is closer to 1, a clustering effect is better; ifs is closer to −1, a clustering effect is worse; the main color extraction method described above is to calculate the silhouette coefficient s using different K values; in a clustering process using different K values, determine an s that is closest to 1 to be a K value representing the best clustering effect; and complete the clustering process using the best K value, and calculate a K-Means objective function J.