Color reduction for Machine-Printed Carpet Pattern by Reinforcement Learning

Automatic reading of carpet patterns Requires To find the original colors of the pattern in a scanned image. It includes detecting of pattern lines and reducing the number of colors in the image. Color reduction is done in two steps: Finding the best pallet and mapping the image colors to the pallet colors. The accuracy of color reduction is so important that it may be required to ask for user intervention. The purpose of this study is to provide a new method in automatic color reduction with high accuracy. To achieve this target, reinforcement learning method is used which yields a 98% accuracy. This is a new method in color reduction and no one has used it yet. This method is defined with respect to the application and the amount of color reduction is such that does not degrade the accuracy. Therefore, the resulting pallet has more colors comparing to the original one. In the work reported in this article, first the grid lines of the pattern are detected. Then a single color is assigned to each box of the grid. After these steps, through the reinforcement learning method the color reduction is carried out. The results obtained from applying the proposed algorithm on some sample images are reported and discussed.