Transportation scene recognition based on high level feature representation
With the development of intelligent transportation,it has become an urgent problem to quickly and accurately recognize complex traffic scene.In recent years,a large number of scene recognition methods have been proposed to improve the effectiveness of traffic scene recognition,however,most of these...
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| Format: | Article |
| Language: | zho |
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POSTS&TELECOM PRESS Co., LTD
2019-12-01
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| Series: | 智能科学与技术学报 |
| Subjects: | |
| Online Access: | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.201943 |
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| _version_ | 1846171160686886912 |
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| author | Wenhua LIU Yidong LI Tao WANG Jun WU Yi JIN |
| author_facet | Wenhua LIU Yidong LI Tao WANG Jun WU Yi JIN |
| author_sort | Wenhua LIU |
| collection | DOAJ |
| description | With the development of intelligent transportation,it has become an urgent problem to quickly and accurately recognize complex traffic scene.In recent years,a large number of scene recognition methods have been proposed to improve the effectiveness of traffic scene recognition,however,most of these algorithms cannot extract the semantic characteristics of the concept of vision,leading to the low recognition accuracy in traffic scenes.Therefore,a novel traffic scene recognition algorithm which extracts the high-level semantic and structural information for improving the accuracy was proposed.A system to discover semantically meaningful descriptions of the scene classes to reduce the “semantic gap” between the high level and the low-level feature representation was built.Then,the multi-label network was trained by minimizing loss function (namely,element-wise logistic loss) to obtain the high-level semantic representation of traffic scene images.Finally,experiments on four large-scale scene recognition datasets show that the proposed algorithm considerably outperforms other state-of-the-art methods. |
| format | Article |
| id | doaj-art-f8b37d622a734558af9affaf02f023a5 |
| institution | Kabale University |
| issn | 2096-6652 |
| language | zho |
| publishDate | 2019-12-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 智能科学与技术学报 |
| spelling | doaj-art-f8b37d622a734558af9affaf02f023a52024-11-11T06:51:29ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522019-12-01139239959636794Transportation scene recognition based on high level feature representationWenhua LIUYidong LITao WANGJun WUYi JINWith the development of intelligent transportation,it has become an urgent problem to quickly and accurately recognize complex traffic scene.In recent years,a large number of scene recognition methods have been proposed to improve the effectiveness of traffic scene recognition,however,most of these algorithms cannot extract the semantic characteristics of the concept of vision,leading to the low recognition accuracy in traffic scenes.Therefore,a novel traffic scene recognition algorithm which extracts the high-level semantic and structural information for improving the accuracy was proposed.A system to discover semantically meaningful descriptions of the scene classes to reduce the “semantic gap” between the high level and the low-level feature representation was built.Then,the multi-label network was trained by minimizing loss function (namely,element-wise logistic loss) to obtain the high-level semantic representation of traffic scene images.Finally,experiments on four large-scale scene recognition datasets show that the proposed algorithm considerably outperforms other state-of-the-art methods.http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.201943scene recognition;CNN;high-level feature;low-level feature |
| spellingShingle | Wenhua LIU Yidong LI Tao WANG Jun WU Yi JIN Transportation scene recognition based on high level feature representation 智能科学与技术学报 scene recognition;CNN;high-level feature;low-level feature |
| title | Transportation scene recognition based on high level feature representation |
| title_full | Transportation scene recognition based on high level feature representation |
| title_fullStr | Transportation scene recognition based on high level feature representation |
| title_full_unstemmed | Transportation scene recognition based on high level feature representation |
| title_short | Transportation scene recognition based on high level feature representation |
| title_sort | transportation scene recognition based on high level feature representation |
| topic | scene recognition;CNN;high-level feature;low-level feature |
| url | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.201943 |
| work_keys_str_mv | AT wenhualiu transportationscenerecognitionbasedonhighlevelfeaturerepresentation AT yidongli transportationscenerecognitionbasedonhighlevelfeaturerepresentation AT taowang transportationscenerecognitionbasedonhighlevelfeaturerepresentation AT junwu transportationscenerecognitionbasedonhighlevelfeaturerepresentation AT yijin transportationscenerecognitionbasedonhighlevelfeaturerepresentation |