TY - JOUR
T1 - On the relevance of the metadata used in the semantic segmentation of indoor image spaces
AU - Vasquez-Espinoza, Luis
AU - Castillo-Cara, Manuel
AU - Orozco-Barbosa, Luis
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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.
AB - 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.
KW - Deep learning
KW - Fully convolutional network
KW - Indoor scenes
KW - Metadata preprocessing
KW - Semantic segmentation
KW - U-net
UR - https://hdl.handle.net/20.500.12724/13669
UR - http://www.scopus.com/inward/record.url?scp=85109921392&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2021.115486
DO - 10.1016/j.eswa.2021.115486
M3 - Artículo (Contribución a Revista)
AN - SCOPUS:85109921392
SN - 0957-4174
VL - 184
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 115486
ER -