Organizing Cultural Heritage with Deep Features

In recent years, the preservation and diffusion of culture in the digital form has been a priority for the governments in different countries, as in Mexico, with the objective of preserving and spreading culture through information technologies. Nowadays, a large amount of multimedia content is produced. Therefore, more efficient and accurate systems are required to organize it. In this work, we analyze the ability of a pre-trained residual network (ResNet) to describe information through the extracted deep features and we analyze its behavior by grouping new data into clusters by the K-means method at different levels of compression with the PCA algorithm showing that the structuring of new input data can be done with the proposed method.