During the latest years, deep learning techniques have demonstrated their capability on surpassing traditional machine learning methods on performing complex pattern recognition tasks. In this post we will try to explain the reasons for that.
Traditional machine learning paradigm is based on feature extraction and feature selection. These features are normally designed by experts with a good domain knowledge. In this sense, computer vision based machine learning systems made use of, for example, textural features such as Gabor filter banks, Local Binary Patterns (LBP) in order to extract textural information or histogram based features or color model transformations in order to describe the color on the image. Continuar leyendo