## 4 Experimental Results and Discussion
### Application for Texture Classification  
Next, each texture sample was classified by using the optimal parameters and features of Gabor filter. Figures 5-7 indicate the difference in the average eigenvalues between the training and testing samples for the categories with the highest classification accuracy using the selected parameters and features of Gabor filter. Tables 3-5 show the classification accuracy using the newly proposed Log-Gabor filter [44], DS-Gabor filter [18], the only parameter optimization-based Gabor (OP-Gabor) filter, and the proposed method. In the tables, OA and Kappa are the overall classification accuracy and Kappa coefficient, respectively, obtained by using different texture classification methods. The Kappa coefficient is defined below:  
\[k=\frac{P_{o}-P_{e}}{1-P_{e}} \tag{12}\]  
where \(P_{o}\) is the relative observed agreement, and \(P_{e}\) is the hypothetical probability of chance agreement.  
Tables 3-5 reveal that the classification accuracy was increased, with a difference of more than 2%, by removing some redundant features of Gabor filter. In addition, more than 40 samples were misclassified for the Kylberg database. Although the classification accuracy for the Leather, Road, and Wood classes in the CGT database was relatively high, misclassification still had a certain influence on the overall process. Moreover, the Kappa coefficient was more than 0.93 for all databases, illustrating that the precision could adapt to a number of application demands. The difference in the average eigenvalues between the training and testing samples was lower than 0.03, and the change trend was similar to that shown in Figures 5-7, thus proving the discriminability and identity of each Gabor filter. The overall classification accuracy of the Log-Gabor filter and DS-Gabor filter was unsatisfactory: it was lower than 85% for the CGT database and only 86.56% and 82.58%, respectively, for the Kylberg database.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline
**Category** & **Log-Gabor** & **DS-Gabor** & **OP-Gabor** & **Proposed** & **Category** & **Log-Gabor** & **DS-Gabor** & **OP-Gabor** & **Proposed** \\ \hline D1 & 87.50 & 97.92 & 97.92 & 97.92 & D49 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ D3 & 75.00 & 97.92 & 97.92 & 97.92 & D52 & 85.42 & 93.75 & 97.92 & 97.92 \\ D6 & 97.92 & 95.83 & 97.92 & 97.92 & D56 & 95.83 & 89.58 & 95.83 & 95.83 \\ D10 & 100.00 & 100.00 & 100.00 & 100.00 & D64 & 85.42 & 87.50 & 97.92 & 97.92 \\ D11 & 97.92 & 97.92 & 100.00 & 100.00 & D66 & 89.58 & 97.92 & 97.92 & 97.92 \\ D14 & 97.92 & 93.75 & 97.92 & 97.92 & D74 & 95.83 & 95.83 & 95.83 & 95.83 \\ D18 & 81.25 & 91.67 & 93.75 & 93.75 & D83 & 95.83 & 97.92 & 97.92 & 97.92 \\ D20 & 91.67 & 95.83 & 95.83 & 95.83 & D87 & 98.58 & 87.50 & 95.83 & 95.83 \\ D25 & 95.83 & 95.83 & 95.83 & 95.83 & D88 & 83.33 & 89.58 & 95.83 & 95.83 \\ D31 & 70.83 & 85.42 & 91.67 & 91.67 & D93 & 87.50 & 91.67 & 93.75 & 93.75 \\ D37 & 85.42 & 95.83 & 95.83 & 95.83 & D94 & 62.50 & 81.25 & 89.58 & 89.58 \\ D41 & 97.92 & 97.92 & 97.92 & 97.92 & D99 & 83.33 & 87.50 & 91.67 & 91.67 \\ D46 & 93.75 & 95.83 & 95.83 & 95.83 & D101 & 91.67 & 97.92 & 100.00 & 100.00 \\ D47 & 93.75 & 95.83 & 95.83 & 95.83 & D104 & 93.75 & 89.58 & 95.83 & 95.83 \\ D48 & 85.42 & 95.83 & 95.83 & 95.83 & D111 & 85.42 & 87.50 & 93.75 & 93.75 \\  & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \end{tabular}
\end{table}
Table 5: Overall classification accuracy and Kappa coefficient for Brodatz database.  
Figure 5: Difference of average eigenvalues between training and testing samples for CGT database.  
Figure 6: Difference of average eigenvalues between training and testing samples for Kylberg database.  
database. With these two methods, very few samples were correctly classified for the Wheel, Brick, Sand, and Stone slab categories, thus it was difficult for them to extract texture features from the whole database. In brief, the proposed approach is a reliable, efficient, and reasonable method for texture classification.