Optimizing Color Detection in Digital Images: A Machine Learning Approach Using K-Nearest Neighbor
Keywords:
Color detection, Machine learning, KNN, Digital image, RGB, OpenCVAbstract
Real-world problem-solving using machine learning algorithms, whether they are supervised or unsupervised, has become a significant issue. In this case, the system is trained with provided data to make predictions based on that data or previous experience. To recognize any object, color detection is necessary and machine learning has been proven benificial in this regard. However, color detection means identifying color name. This study utilizes the supervised ML algorithm K-Nearest Neighbor to optimize color detection in digital (RGB) images. This algorithm is used to separate different colors in the RGB images. This algorithm is beneficial for classification and regression (nonlinear) and provides advantages to some extent over other algorithms. A dataset containing 818 color names with their respective r, g, b values along with the intensity of each color has been trained with the KNN algorithm. By using the feature similarity of the nearest data points, it predicts the new data points` values. Initially, it calculates the distance between new and each training point, then selects the nearest points upon the value of K. The experimentation showed that a notable accuracy of 93.089% has been achieved with the optimal number of neighbors set at K=5. As computers can only track three basic colors (RGB), the OpenCV library is used to help detecting colors based on this fundamental color combination at different intensities.
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