Image annotation for Mexican buildings database

In the computer world, the consumption and generation of multimedia content are in constant growth due to the popularization of mobile devices and new communication technologies. Retrieve information from multimedia content to describe Mexican buildings is a challenging problem. Our objective is to determine patterns related to three building eras (prehispanic, colonial and modern). For this purpose, existing recognition systems need to process plenty of videos and images. The automatic systems based on machine learning trains the recognition capability with a semantically annotated training database. We built the database taking into account high-level feature concepts, user knowledge and experience. The annotations help correlating context and content to understand the data in multimedia files. Without a method, the user needs a super mind to remember all and registry this data manually. This article presents a ethodology for a quick image annotation using a graphical interface and intuitive controls. We focus on the most two important features: time-consuming during annotations task and the quality of selected images. Though, we only annotate images by its era and its quality. Finally, we obtain a dataset of Mexican buildings preserving the contextual information with semantic annotations for training and test of building recognition systems. Furthermore, research on content low-level descriptors is other possible use for this dataset.