# Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm +
## 2 Related Work  
Color classification of wooden boards is important to improve the quality of wooden furniture where the boards with a similar color are preferred to splice together.  
Quantitative color analysis is used to classify the colors of wood. In [2], the author evaluated walnut wood color using the international CIE (Commission International de l'Eclairage) method and found that luminosity can be used to discriminate color in walnut wood. In [3], the surface color region of seven wood species was determined by the analysis of RGB color components, and a specific color of each wood species was used to identify wood species present in a chip mixture. In [7], the authors found that the three parameters (L*, a*, and b*) are adequate for the classification of thermally-modified ash and beech hardwood according to the analysis of the wood color with the CIE L*a*b* color space (L* for the lightness from black to white, a* from green to red, and b* from blue to yellow). In [8], the authors used the CIE L*a*b* system to discriminate the variability of the wood color and determined the correlation with the wood's basic density for ten Amazonian tree species.  
Machine learning algorithms have become popular solutions for signal processing [9; 10; 11]. They have also been used for color classification of wooden boards. In [6], the authors studied the supervised machine learning methods for wood classification based on the main color characteristic obtained through the HSV color model and co-occurrence matrix characteristic. In [12], the average RGB histogram value for each color channel and the static characteristics of the gray-level co-occurrence matrix were used as features to identify the strength of wood. In [13], the SVM (support vector machine) model achieved an accuracy of 0.960 for classification of thermally-modified wood using the color lightness parameter as a feature.  
In addition, approaches related to the classification of other color images were proposed. In [4], the k-nearest neighbor (k-NN) classification principle was used to classify rock images based on color features in the Gabor space. Reference [14] selected eight color features of the images extracted in the HSV, HSL (hue, saturation, lightness), and HSI (hue, saturation, intensity) color spaces to classify farmland images with the k-NN algorithm. In [15], the authors used SVM to study the performance of color histograms in different color spaces for content based color image classification. Reference [5] examined the performance of a wide range of classifiers in recognizing induced emotions using the color histogram as a single feature, and different numbers of color histogram bins in both RGB and HSV color spaces were considered in the examination. In [16], the red, green, and blue color components of the RGB color model were selected as features to classify the different groups of materials with supervised machine learning algorithms.  
However, due to the color similarity among wooden boards, the supervised learning algorithms can hardly be used for the color classification of wooden boards. To solve this problem, the clustering algorithms may be a reasonable solution, which has been used to process images in some applications. Reference [17] used the k-means algorithm to classify galaxies into morphological clusters by their visual similarity. In [18], the G-means algorithm was used for intrusion detection. In [19], the authors adopted the fuzzy c-means clustering algorithm for color image segmentation.  
Nevertheless, to the best of our knowledge, there is no related work that has used the clustering algorithm for color classification of wooden boards. The images of wooden boards have their own characteristics; thus, related works cannot be used directly. This motivates our work, which will be described in the following sections.