Comparative study of building recognition rates in urban environments using vector quantization and deep learning
Building recognition is essential for a variety of applications such as automatic target detection, 3D city reconstruction, digital navigation, etc. This work aims to comparatively analyze the recognition rates of building images, using the Vector Quantization technique for image compression using the Linde-Buzo-Gray algorithm, with the results obtained by the Deep Learning method.