info:eu-repo/semantics/article
On the relevance of the metadata used in the semantic segmentation of indoor image spaces
Fecha
2021Registro en:
0957-4174
Expert Systems with Applications
0000000121541816
Autor
Vasquez Espinoza L.
Castillo Cara, José Manuel
Orozco Barbosa L.
Institución
Resumen
The study of artificial learning processes in the area of computer vision context has mainly focused on achieving a fixed output target rather than on identifying the underlying processes as a means to develop solutions capable of performing as good as or better than the human brain. This work reviews the well-known segmentation efforts in computer vision. However, our primary focus is on the quantitative evaluation of the amount of contextual information provided to the neural network. In particular, the information used to mimic the tacit information that a human is capable of using, like a sense of unambiguous order and the capability of improving its estimation by complementing already learned information. Our results show that, after a set of pre and post-processing methods applied to both the training data and the neural network architecture, the predictions made were drastically closer to the expected output in comparison to the cases where no contextual additions were provided. Our results provide evidence that learning systems strongly rely on contextual information for the identification task process.