Automated Design of Salient Object Detection Algorithms with Brain Programming
Despite recent improvements in computer vision, artificial visual systems’ design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain’s inner workings. Progr...
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MDPI AG
2022-10-01
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Series: | Applied Sciences |
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Online Access: | https://www.mdpi.com/2076-3417/12/20/10686 |
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author | Gustavo Olague Jose Armando Menendez-Clavijo Matthieu Olague Arturo Ocampo Gerardo Ibarra-Vazquez Rocio Ochoa Roberto Pineda |
author_facet | Gustavo Olague Jose Armando Menendez-Clavijo Matthieu Olague Arturo Ocampo Gerardo Ibarra-Vazquez Rocio Ochoa Roberto Pineda |
author_sort | Gustavo Olague |
collection | DOAJ |
description | Despite recent improvements in computer vision, artificial visual systems’ design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain’s inner workings. Progress in this research area follows the traditional path of hand-made designs using neuroscience knowledge or, more recently, deep learning, a particular branch of machine learning. Recently, a different approach based on genetic programming appeared to enhance handcrafted techniques following two different strategies. The first method follows the idea of combining previous hand-made methods through genetic programming and fuzzy logic. The second approach improves the inner computational structures of basic hand-made models through artificial evolution. This research proposes expanding the artificial dorsal stream using a recent proposal based on symbolic learning to solve salient object detection problems following the second technique. This approach applies the fusion of visual saliency and image segmentation algorithms as a template. The proposed methodology discovers several critical structures in the template through artificial evolution. We present results on a benchmark designed by experts with outstanding results in an extensive comparison with the state of the art, including classical methods and deep learning approaches to highlight the importance of symbolic learning in visual saliency. |
first_indexed | 2024-03-09T20:45:14Z |
format | Article |
id | doaj.art-f183c7f9182641e1bfa3be5fa8b570f4 |
institution | Directory Open Access Journal |
issn | 2076-3417 |
language | English |
last_indexed | 2024-03-09T20:45:14Z |
publishDate | 2022-10-01 |
publisher | MDPI AG |
record_format | Article |
series | Applied Sciences |
spelling | doaj.art-f183c7f9182641e1bfa3be5fa8b570f42023-11-23T22:48:46ZengMDPI AGApplied Sciences2076-34172022-10-0112201068610.3390/app122010686Automated Design of Salient Object Detection Algorithms with Brain ProgrammingGustavo Olague0Jose Armando Menendez-Clavijo1Matthieu Olague2Arturo Ocampo3Gerardo Ibarra-Vazquez4Rocio Ochoa5Roberto Pineda6CICESE Research Center, EvoVisión Laboratory, Department of Computer Science, Carretera Tijuana-Ensenada 3918, Zona Playitas, Ensenada C.P. 22860, MexicoCICESE Research Center, EvoVisión Laboratory, Department of Computer Science, Carretera Tijuana-Ensenada 3918, Zona Playitas, Ensenada C.P. 22860, MexicoMechatronics Engineering Faculty, Anáhuac University–Queretaro, Calle Circuito Universidades I, Kilómetro 7, Fracción 2, El Marqués, Queretaro C.P. 76246, MexicoFaculty of Higher Studies Aragón, National Autonomous University of Mexico, Av Hacienda de Rancho Seco S/N, Impulsora Popular Avicola, Nezahualcóyotl C.P. 57130, MexicoFacultad de Ingeniería, Autonomous University of San Luis Potosí, Dr. Manuel Nava 8, Col. Zona Universitaria Poniente, San Luis Potosí C.P. 78290, MexicoFacultad de Ciencias Básicas Ingeniería y Tecnología, Autonomous University of Tlaxcala, Carretera Apizaquito S/N, San Luis Apizaquito, Apizaco C.P. 90401, MexicoCICESE Research Center, EvoVisión Laboratory, Department of Computer Science, Carretera Tijuana-Ensenada 3918, Zona Playitas, Ensenada C.P. 22860, MexicoDespite recent improvements in computer vision, artificial visual systems’ design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain’s inner workings. Progress in this research area follows the traditional path of hand-made designs using neuroscience knowledge or, more recently, deep learning, a particular branch of machine learning. Recently, a different approach based on genetic programming appeared to enhance handcrafted techniques following two different strategies. The first method follows the idea of combining previous hand-made methods through genetic programming and fuzzy logic. The second approach improves the inner computational structures of basic hand-made models through artificial evolution. This research proposes expanding the artificial dorsal stream using a recent proposal based on symbolic learning to solve salient object detection problems following the second technique. This approach applies the fusion of visual saliency and image segmentation algorithms as a template. The proposed methodology discovers several critical structures in the template through artificial evolution. We present results on a benchmark designed by experts with outstanding results in an extensive comparison with the state of the art, including classical methods and deep learning approaches to highlight the importance of symbolic learning in visual saliency.https://www.mdpi.com/2076-3417/12/20/10686visual attentiongenetic programmingsalient object detection |
spellingShingle | Gustavo Olague Jose Armando Menendez-Clavijo Matthieu Olague Arturo Ocampo Gerardo Ibarra-Vazquez Rocio Ochoa Roberto Pineda Automated Design of Salient Object Detection Algorithms with Brain Programming Applied Sciences visual attention genetic programming salient object detection |
title | Automated Design of Salient Object Detection Algorithms with Brain Programming |
title_full | Automated Design of Salient Object Detection Algorithms with Brain Programming |
title_fullStr | Automated Design of Salient Object Detection Algorithms with Brain Programming |
title_full_unstemmed | Automated Design of Salient Object Detection Algorithms with Brain Programming |
title_short | Automated Design of Salient Object Detection Algorithms with Brain Programming |
title_sort | automated design of salient object detection algorithms with brain programming |
topic | visual attention genetic programming salient object detection |
url | https://www.mdpi.com/2076-3417/12/20/10686 |
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