Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model
In order to improve the accuracy of weakly-supervised semantic segmentation method,a segmentation and optimization algorithm that combines multi-scale feature was proposed.The new algorithm firstly constructs a multi-scale feature model based on transfer learning algorithm.In addition,a new classifi...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019004/ |
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| author | Changzhen XIONG Hui ZHI |
| author_facet | Changzhen XIONG Hui ZHI |
| author_sort | Changzhen XIONG |
| collection | DOAJ |
| description | In order to improve the accuracy of weakly-supervised semantic segmentation method,a segmentation and optimization algorithm that combines multi-scale feature was proposed.The new algorithm firstly constructs a multi-scale feature model based on transfer learning algorithm.In addition,a new classifier was introduced for category prediction to reduce the failure of segmentation due to the prediction of target class information errors.Then the designed multi-scale model was fused with the original transfer learning model by different weights to enhance the generalization performance of the model.Finally,the predictions class credibility was added to adjust the credibility of the corresponding class of pixels in the segmentation map,avoiding false positive segmentation regions.The proposed algorithm was tested on the challenging VOC 2012 dataset,the mean intersection-over-union is 58.8% on validation dataset and 57.5% on test dataset.It outperforms the original transfer-learning algorithm by 12.9% and 12.3%.And it performs favorably against other segmentation methods using weakly-supervised information based on category labels as well. |
| format | Article |
| id | doaj-art-9ac93370463c49eaadc28d339ddb3d25 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-9ac93370463c49eaadc28d339ddb3d252025-01-14T07:16:11ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-01-014016317159724723Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature modelChangzhen XIONGHui ZHIIn order to improve the accuracy of weakly-supervised semantic segmentation method,a segmentation and optimization algorithm that combines multi-scale feature was proposed.The new algorithm firstly constructs a multi-scale feature model based on transfer learning algorithm.In addition,a new classifier was introduced for category prediction to reduce the failure of segmentation due to the prediction of target class information errors.Then the designed multi-scale model was fused with the original transfer learning model by different weights to enhance the generalization performance of the model.Finally,the predictions class credibility was added to adjust the credibility of the corresponding class of pixels in the segmentation map,avoiding false positive segmentation regions.The proposed algorithm was tested on the challenging VOC 2012 dataset,the mean intersection-over-union is 58.8% on validation dataset and 57.5% on test dataset.It outperforms the original transfer-learning algorithm by 12.9% and 12.3%.And it performs favorably against other segmentation methods using weakly-supervised information based on category labels as well.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019004/deep learningweakly-supervised learningmodel integrationmulti-scale featuremodel optimization |
| spellingShingle | Changzhen XIONG Hui ZHI Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model Tongxin xuebao deep learning weakly-supervised learning model integration multi-scale feature model optimization |
| title | Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model |
| title_full | Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model |
| title_fullStr | Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model |
| title_full_unstemmed | Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model |
| title_short | Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model |
| title_sort | weakly supervised semantic segmentation and optimization algorithm based on multi scale feature model |
| topic | deep learning weakly-supervised learning model integration multi-scale feature model optimization |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019004/ |
| work_keys_str_mv | AT changzhenxiong weaklysupervisedsemanticsegmentationandoptimizationalgorithmbasedonmultiscalefeaturemodel AT huizhi weaklysupervisedsemanticsegmentationandoptimizationalgorithmbasedonmultiscalefeaturemodel |