Convolutional neural network with multi-column characteristics extraction for image classification
In the last few decades, the constant growth of digital images, as the main source of information representation for scientific applications, has made image classification a challenging task. To achieve high classification yields, different pattern recognition techniques have been proposed, among wh...
- Autores:
-
Silva, Jesús
Varela Izquierdo, Noel
Patiño-Saucedo, Janns A.
Pineda, Omar
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7256
- Acceso en línea:
- https://hdl.handle.net/11323/7256
https://repositorio.cuc.edu.co/
- Palabra clave:
- Convolutional neural network
Image classification
Multi-column characteristics extraction
- Rights
- closedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Convolutional neural network with multi-column characteristics extraction for image classification |
| title |
Convolutional neural network with multi-column characteristics extraction for image classification |
| spellingShingle |
Convolutional neural network with multi-column characteristics extraction for image classification Convolutional neural network Image classification Multi-column characteristics extraction |
| title_short |
Convolutional neural network with multi-column characteristics extraction for image classification |
| title_full |
Convolutional neural network with multi-column characteristics extraction for image classification |
| title_fullStr |
Convolutional neural network with multi-column characteristics extraction for image classification |
| title_full_unstemmed |
Convolutional neural network with multi-column characteristics extraction for image classification |
| title_sort |
Convolutional neural network with multi-column characteristics extraction for image classification |
| dc.creator.fl_str_mv |
Silva, Jesús Varela Izquierdo, Noel Patiño-Saucedo, Janns A. Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Varela Izquierdo, Noel Patiño-Saucedo, Janns A. Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Convolutional neural network Image classification Multi-column characteristics extraction |
| topic |
Convolutional neural network Image classification Multi-column characteristics extraction |
| description |
In the last few decades, the constant growth of digital images, as the main source of information representation for scientific applications, has made image classification a challenging task. To achieve high classification yields, different pattern recognition techniques have been proposed, among which are the deep learning methods that today focus their study on image processing and computer vision. In this approach, the most popular architecture for the image classification task is the convolutional neural network (CNN), a network constructed of multiple layers and where each layer models a receptive field of the visual cortex making it much more effective in artificial vision tasks [1]. This paper proposes a convolutional network architecture with a performance-enhancing approach, a hierarchical structure that is easy to build, adaptive, and easy to train with good performance in image classification tasks. |
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2020 |
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2020-11-11T16:42:31Z |
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2020-11-11T16:42:31Z |
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2020 |
| dc.date.embargoEnd.none.fl_str_mv |
2021-05-07 |
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Pre-Publicación |
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http://purl.org/coar/resource_type/c_816b |
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Text |
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2194-5357 |
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https://hdl.handle.net/11323/7256 |
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Corporación Universidad de la Costa |
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eng |
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Wang, H., Ding, S., Wu, D., Zhang, Y., Yang, S.: Smart connected electronic gastroscope system for gastric cancer screening using multi-column convolutional neural networks. Int. J. Prod. Res. 57(21), 6795–6806 (2019) Wang, Y., Hu, S., Wang, G., Chen, C., Pan, Z.: Multi-scale dilated convolution of convolutional neural network for crowd counting. Multimedia Tools Appl. 79(1), 1057–1073 (2019). https://ezproxy.cuc.edu.co:2067/10.1007/s11042-019-08208-6 Li, Z., Zhou, A., Shen, Y.: An end-to-end trainable multi-column CNN for scene recognition in extremely changing environment. Sensors 20(6), 1556 (2020) Hu, Y., Lu, M., Lu, X.: Feature refinement for image-based driver action recognition via multi-scale attention convolutional neural network. Sig. Process. Image Commun. 81, 115697 (2020) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) CireşAn, D., Meier, U., Masci, J., Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012) Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014) Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019) Yu, D., Wang, H., Chen, P., Wei, Z.: Mixed pooling for convolutional neural networks. In: International Conference on Rough Sets and Knowledge Technology, pp. 364–375. Springer, Cham (2014) Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020) Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649. IEEE (2012) Oliva, T.: http://cvcl.mit.edu/database.htm, May 2016 Khosla, A., Nityananda, J., Yao, B., Fei-Fei, L.: Stanford Dogs Dataset, September 2017. http://vision.stanford.edu/aditya86/ImageNetDogs/ Caltech256: Caltech 256 Dataset, May 2016. www.vision.caltech.edu/ImageDatasets/Caltech256 Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597 (2016) Cireşan, D., Meier, U.: Multi-column deep neural networks for offline handwritten Chinese character classification. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2015) Lu, X., Lin, Z., Shen, X., Mech, R., Wang, J.Z.: Deep multi-patch aggregation network for image style, aesthetics, and quality estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 990–998 (2015) Ke, Q., Ming, L.D., Daxing, Z.: Image steganalysis via multi-column convolutional neural network. In: 2018 14th IEEE International Conference on signal processing (ICSP), pp. 550–553. IEEE (2018) McDonnell, M.D., Vladusich, T.: Enhanced image classification with a fast-learning shallow convolutional neural network. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE (2015) Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. ANT/EDI40, pp. 1201–1206 (2019) Yang, W., Jin, L., Xie, Z., Feng, Z.: Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 551–555. IEEE (2015) Jmour, N., Zayen, S., Abdelkrim, A.: Convolutional neural networks for image classification. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), pp. 397–402. IEEE (2018) Park, T., Lee, T.: Musical instrument sound classification with deep convolutional neural network using feature fusion approach (2015). arXiv preprint arXiv:1512.07370 Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using Googlenet and directional feature maps. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE (2015) Du, J., Zhai, J.F., Hu, J.S., Zhu, B., Wei, S., Dai, L.R.: Writer adaptive feature extraction based on convolutional neural networks for online handwritten Chinese character recognition. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 841–845. IEEE (2015) Bindhu, V.: Biomedical image analysis using semantic segmentation. J. Innov. Image Process. (JIIP) 1(02), 91–101 (2019) Sharma, N., Jain, V., Mishra, A.: An analysis of convolutional neural networks for image classification. Procedia Comput. Sci. 132, 377–384 (2018) Yim, J., Sohn, K.A.: Enhancing the performance of convolutional neural networks on quality degraded datasets. In: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2017) Verma, A., Vig, L.: Using convolutional neural networks to discover cogntively validated features for gender classification. In: 2014 International Conference on Soft Computing and Machine Intelligence, pp. 33–37. IEEE (2014) Zeng, Y., Xu, X., Fang, Y., Zhao, K.: Traffic sign recognition using extreme learning classifier with deep convolutional features. In: The 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015), Suzhou, China, vol. 9242, pp. 272–280 (2015) |
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Silva, JesúsVarela Izquierdo, NoelPatiño-Saucedo, Janns A.Pineda, Omar2020-11-11T16:42:31Z2020-11-11T16:42:31Z20202021-05-072194-5357https://hdl.handle.net/11323/7256Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In the last few decades, the constant growth of digital images, as the main source of information representation for scientific applications, has made image classification a challenging task. To achieve high classification yields, different pattern recognition techniques have been proposed, among which are the deep learning methods that today focus their study on image processing and computer vision. In this approach, the most popular architecture for the image classification task is the convolutional neural network (CNN), a network constructed of multiple layers and where each layer models a receptive field of the visual cortex making it much more effective in artificial vision tasks [1]. This paper proposes a convolutional network architecture with a performance-enhancing approach, a hierarchical structure that is easy to build, adaptive, and easy to train with good performance in image classification tasks.Silva, JesúsVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Patiño-Saucedo, Janns A.Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAdvances in Intelligent Systems and Computinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089236554&doi=10.1007%2f978-3-030-51859-2_3&partnerID=40&md5=4dcdff0ce39830f7a508a88da19ea169Convolutional neural networkImage classificationMulti-column characteristics extractionConvolutional neural network with multi-column characteristics extraction for image classificationPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionWang, H., Ding, S., Wu, D., Zhang, Y., Yang, S.: Smart connected electronic gastroscope system for gastric cancer screening using multi-column convolutional neural networks. Int. J. Prod. Res. 57(21), 6795–6806 (2019)Wang, Y., Hu, S., Wang, G., Chen, C., Pan, Z.: Multi-scale dilated convolution of convolutional neural network for crowd counting. Multimedia Tools Appl. 79(1), 1057–1073 (2019). https://ezproxy.cuc.edu.co:2067/10.1007/s11042-019-08208-6Li, Z., Zhou, A., Shen, Y.: An end-to-end trainable multi-column CNN for scene recognition in extremely changing environment. Sensors 20(6), 1556 (2020)Hu, Y., Lu, M., Lu, X.: Feature refinement for image-based driver action recognition via multi-scale attention convolutional neural network. Sig. Process. Image Commun. 81, 115697 (2020)LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)CireşAn, D., Meier, U., Masci, J., Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)Yu, D., Wang, H., Chen, P., Wei, Z.: Mixed pooling for convolutional neural networks. In: International Conference on Rough Sets and Knowledge Technology, pp. 364–375. Springer, Cham (2014)Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020)Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3642–3649. IEEE (2012)Oliva, T.: http://cvcl.mit.edu/database.htm, May 2016Khosla, A., Nityananda, J., Yao, B., Fei-Fei, L.: Stanford Dogs Dataset, September 2017. http://vision.stanford.edu/aditya86/ImageNetDogs/Caltech256: Caltech 256 Dataset, May 2016. www.vision.caltech.edu/ImageDatasets/Caltech256Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597 (2016)Cireşan, D., Meier, U.: Multi-column deep neural networks for offline handwritten Chinese character classification. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2015)Lu, X., Lin, Z., Shen, X., Mech, R., Wang, J.Z.: Deep multi-patch aggregation network for image style, aesthetics, and quality estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 990–998 (2015)Ke, Q., Ming, L.D., Daxing, Z.: Image steganalysis via multi-column convolutional neural network. In: 2018 14th IEEE International Conference on signal processing (ICSP), pp. 550–553. IEEE (2018)McDonnell, M.D., Vladusich, T.: Enhanced image classification with a fast-learning shallow convolutional neural network. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE (2015)Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-Means for innovation databases in SMEs. ANT/EDI40, pp. 1201–1206 (2019)Yang, W., Jin, L., Xie, Z., Feng, Z.: Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 551–555. IEEE (2015)Jmour, N., Zayen, S., Abdelkrim, A.: Convolutional neural networks for image classification. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), pp. 397–402. IEEE (2018)Park, T., Lee, T.: Musical instrument sound classification with deep convolutional neural network using feature fusion approach (2015). arXiv preprint arXiv:1512.07370Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using Googlenet and directional feature maps. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE (2015)Du, J., Zhai, J.F., Hu, J.S., Zhu, B., Wei, S., Dai, L.R.: Writer adaptive feature extraction based on convolutional neural networks for online handwritten Chinese character recognition. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 841–845. IEEE (2015)Bindhu, V.: Biomedical image analysis using semantic segmentation. J. Innov. Image Process. (JIIP) 1(02), 91–101 (2019)Sharma, N., Jain, V., Mishra, A.: An analysis of convolutional neural networks for image classification. Procedia Comput. Sci. 132, 377–384 (2018)Yim, J., Sohn, K.A.: Enhancing the performance of convolutional neural networks on quality degraded datasets. In: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2017)Verma, A., Vig, L.: Using convolutional neural networks to discover cogntively validated features for gender classification. In: 2014 International Conference on Soft Computing and Machine Intelligence, pp. 33–37. IEEE (2014)Zeng, Y., Xu, X., Fang, Y., Zhao, K.: Traffic sign recognition using extreme learning classifier with deep convolutional features. In: The 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015), Suzhou, China, vol. 9242, pp. 272–280 (2015)PublicationORIGINALCONVOLUTIONAL NEURAL NETWORK WITH MULTI-COLUMN CHARACTERISTICS EXTRACTION FOR IMAGE CLASSIFICATION.pdfCONVOLUTIONAL NEURAL NETWORK WITH MULTI-COLUMN CHARACTERISTICS EXTRACTION FOR IMAGE CLASSIFICATION.pdfapplication/pdf6381https://repositorio.cuc.edu.co/bitstreams/a4cd393f-0633-4e38-b082-0bd0abd1dd14/downloada9d3434f9aae91026333fc1bb4c4c890MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/7de9ad6e-a83d-41f6-af90-aa226609e0ff/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/4153681c-eb47-43f1-8b92-f97cdca252c4/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILCONVOLUTIONAL NEURAL NETWORK WITH MULTI-COLUMN CHARACTERISTICS EXTRACTION FOR IMAGE 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