Review of image classification based on deep learning
In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2019-11-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019268/ |
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| _version_ | 1841530619098562560 |
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| author | Fu SU Qin LV Renze LUO |
| author_facet | Fu SU Qin LV Renze LUO |
| author_sort | Fu SU |
| collection | DOAJ |
| description | In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed. |
| format | Article |
| id | doaj-art-bbe88670ddab4f0ebb689f294cc0801d |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2019-11-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-bbe88670ddab4f0ebb689f294cc0801d2025-01-15T03:02:00ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012019-11-0135587459586056Review of image classification based on deep learningFu SUQin LVRenze LUOIn recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019268/deep learningimage classificationauto-encodersdeep belief networksCNN |
| spellingShingle | Fu SU Qin LV Renze LUO Review of image classification based on deep learning Dianxin kexue deep learning image classification auto-encoders deep belief networks CNN |
| title | Review of image classification based on deep learning |
| title_full | Review of image classification based on deep learning |
| title_fullStr | Review of image classification based on deep learning |
| title_full_unstemmed | Review of image classification based on deep learning |
| title_short | Review of image classification based on deep learning |
| title_sort | review of image classification based on deep learning |
| topic | deep learning image classification auto-encoders deep belief networks CNN |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019268/ |
| work_keys_str_mv | AT fusu reviewofimageclassificationbasedondeeplearning AT qinlv reviewofimageclassificationbasedondeeplearning AT renzeluo reviewofimageclassificationbasedondeeplearning |