A bio-inspired quaternion local phase CNN layer with contrast invariance and linear sensitivity to rotation angles
Deep learning models have been particularly successful with image recognition using Convolutional Neural Networks (CNN). However, the learning of a contrast invariance and rotation equivariance response may fail even with very deep CNNs or by large data augmentations in training. We were inspired by...
| Authors: | ; ; ; ; ; |
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| Format: | Article |
| Publication date: | 2020 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/422915 |
| Online Access: | https://hdl.handle.net/2117/422915 https://dx.doi.org/10.1016/j.patrec.2019.12.001 |
| Access Level: | Open access |
| Keyword: | Convolution Deep learning Image recognition Rotation Multilayer neural networks Mammals Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
| _version_ | 1878739106805055488 |
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| author | Moya Sánchez, Eduardo Ulises |
| author2 | Xambó Descamps, Sebastián|||0000-0001-5056-9818 Sánchez Pérez, Abraham Salazar Colores, Sebastián Martínez Ortega, Jorge Cortés García, Claudio Ulises|||0000-0003-0192-3096 |
| author2_role | author author author author author |
| author_browse | Cortés García, Claudio Ulises|||0000-0003-0192-3096 Martínez Ortega, Jorge Moya Sánchez, Eduardo Ulises Salazar Colores, Sebastián Sánchez Pérez, Abraham Xambó Descamps, Sebastián|||0000-0001-5056-9818 |
| author_facet | Moya Sánchez, Eduardo Ulises Xambó Descamps, Sebastián|||0000-0001-5056-9818 Sánchez Pérez, Abraham Salazar Colores, Sebastián Martínez Ortega, Jorge Cortés García, Claudio Ulises|||0000-0003-0192-3096 |
| author_role | author |
| collection | UPCommons. Portal del coneixement obert de la UPC |
| description | Deep learning models have been particularly successful with image recognition using Convolutional Neural Networks (CNN). However, the learning of a contrast invariance and rotation equivariance response may fail even with very deep CNNs or by large data augmentations in training. We were inspired by the V1 visual features of the mammalian visual system to emulate as much as possible the early visual system and add more invariant capacities to the CNN. We present a new quaternion local phase convolutional neural network layer encoding three local phases. We present two experimental setups: An image classification task with four contrast levels, and a linear regression task that predicts the rotation angle of an image. In sum, we obtain new patterns and feature representations for deep learning, which capture illumination invariance and a linear response to rotation angles. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_bb3a16faebc8a286b2201a8984edb14f |
| institution | Universitat Politècnica de Catalunya (UPC) |
| instname_str | Universitat Politècnica de Catalunya (UPC) |
| language | eng |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:upcommons.upc.edu:2117/422915 |
| publishDate | 2020 |
| publishDateSort | 2020 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| reponame_str | UPCommons. Portal del coneixement obert de la UPC |
| spelling | A bio-inspired quaternion local phase CNN layer with contrast invariance and linear sensitivity to rotation angles Moya Sánchez, Eduardo Ulises Xambó Descamps, Sebastián|||0000-0001-5056-9818 Sánchez Pérez, Abraham Salazar Colores, Sebastián Martínez Ortega, Jorge Cortés García, Claudio Ulises|||0000-0003-0192-3096 Convolution Deep learning Image recognition Rotation Multilayer neural networks Mammals Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Deep learning models have been particularly successful with image recognition using Convolutional Neural Networks (CNN). However, the learning of a contrast invariance and rotation equivariance response may fail even with very deep CNNs or by large data augmentations in training. We were inspired by the V1 visual features of the mammalian visual system to emulate as much as possible the early visual system and add more invariant capacities to the CNN. We present a new quaternion local phase convolutional neural network layer encoding three local phases. We present two experimental setups: An image classification task with four contrast levels, and a linear regression task that predicts the rotation angle of an image. In sum, we obtain new patterns and feature representations for deep learning, which capture illumination invariance and a linear response to rotation angles. The authors would like to thank to CONACYT and the Barcelona supercomputing Center. Sebastián Salazar-Colores (CVU 477758) would like to thank CONACYT (Consejo Nacional de Ciencia y Tecnología) for the financial support of his PhD studies under Scholarship 285651. Eduardo Ulises Moya-Sánchez, Jorge Martínez-Ortega and Ulises Cortés are members of the Sistema Nacional de Investigadores (SNI) de México. Peer Reviewed Elsevier https://hdl.handle.net/2117/422915 https://dx.doi.org/10.1016/j.patrec.2019.12.001 |
| spellingShingle | A bio-inspired quaternion local phase CNN layer with contrast invariance and linear sensitivity to rotation angles Moya Sánchez, Eduardo Ulises Convolution Deep learning Image recognition Rotation Multilayer neural networks Mammals Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
| title | A bio-inspired quaternion local phase CNN layer with contrast invariance and linear sensitivity to rotation angles |
| title_full | A bio-inspired quaternion local phase CNN layer with contrast invariance and linear sensitivity to rotation angles |
| title_fullStr | A bio-inspired quaternion local phase CNN layer with contrast invariance and linear sensitivity to rotation angles |
| title_full_unstemmed | A bio-inspired quaternion local phase CNN layer with contrast invariance and linear sensitivity to rotation angles |
| title_short | A bio-inspired quaternion local phase CNN layer with contrast invariance and linear sensitivity to rotation angles |
| title_sort | A bio-inspired quaternion local phase CNN layer with contrast invariance and linear sensitivity to rotation angles |
| topic | Convolution Deep learning Image recognition Rotation Multilayer neural networks Mammals Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
| topic_facet | Convolution Deep learning Image recognition Rotation Multilayer neural networks Mammals Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
| url | https://hdl.handle.net/2117/422915 https://dx.doi.org/10.1016/j.patrec.2019.12.001 |