Machine learning methods on quantized vectors

Vector quantization is one of the important issues in digital images. There are many studies conducted on quantized vectors or images. On the other hand, machine-learning approaches are a popular issue today. In this study, the classification performances of machine learning approaches on reduced image vectors are examined. Firstly, Corel 1K data set were reduced to 64 colors with octree and histogram feature vectors extracted. Classification was carried out using various machine learning approaches on the relevant vectors. As a result of the classification, the success of the methods was examined.