A Multi-Label Image Classification Method based on Label Correlation Learning Network
[Purposes] To meet the challenges posed by label feature confusions and limitations in label relationships in multi-label image classification tasks, a novel approach to multi-label image classification based on label correlation learning network (MLLCLN) is presented in this work. [Methods] MLLCLN...
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| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2024-11-01
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| Series: | Taiyuan Ligong Daxue xuebao |
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| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2358.html |
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| _version_ | 1846150334566629376 |
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| author | WANG Lufang ZHANG Haiyun |
| author_facet | WANG Lufang ZHANG Haiyun |
| author_sort | WANG Lufang |
| collection | DOAJ |
| description | [Purposes] To meet the challenges posed by label feature confusions and limitations in label relationships in multi-label image classification tasks, a novel approach to multi-label image classification based on label correlation learning network (MLLCLN) is presented in this work. [Methods] MLLCLN adopts the methods of masked attention approach and multi-head self-attention mechanism. In the masked attention approach, the label features generated by masking the attention mechanism with state word vectors corresponding to the real labels in the image, allowing the model to obtain more contextual information and mitigating the issue of attention overlap in the attention regions. This strategy effectively alleviate the issue of label feature confusion. Moreover, a label correlation learning network is devised, which comprises multiple layers of multi-head attention mechanisms and a graph neural network. On the other hand, the multi-head self-attention mechanism enables the learning of local label relationships according to the label features, while the graph neural network incorporates the widely adopted ML-GCN method to guide the model in considering global label relationships simultaneously, mitigating the issue of false predictions in models caused by the limitations of label relationships. [Findings] The experimental results of MLLCLN on the public datasets MSCOCO2014 and VOC2007 demonstrate its superior performance, achieving classification accuracies of 84.4% and 96.0%, respectively. This provides a novel approach to multi-label image classification. |
| format | Article |
| id | doaj-art-9fb6e007df8141ebb25833003ca5a87b |
| institution | Kabale University |
| issn | 1007-9432 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj-art-9fb6e007df8141ebb25833003ca5a87b2024-11-29T03:39:51ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322024-11-015561097110610.16355/j.tyut.1007-9432.202404461007-9432(2024)06-1097-10A Multi-Label Image Classification Method based on Label Correlation Learning NetworkWANG Lufang0ZHANG Haiyun1Experimental Training Center, Shanxi University of Finance and Economics, Taiyuan 030031, Chinaa. Institute of Big Data Science and Industry, 2b. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China[Purposes] To meet the challenges posed by label feature confusions and limitations in label relationships in multi-label image classification tasks, a novel approach to multi-label image classification based on label correlation learning network (MLLCLN) is presented in this work. [Methods] MLLCLN adopts the methods of masked attention approach and multi-head self-attention mechanism. In the masked attention approach, the label features generated by masking the attention mechanism with state word vectors corresponding to the real labels in the image, allowing the model to obtain more contextual information and mitigating the issue of attention overlap in the attention regions. This strategy effectively alleviate the issue of label feature confusion. Moreover, a label correlation learning network is devised, which comprises multiple layers of multi-head attention mechanisms and a graph neural network. On the other hand, the multi-head self-attention mechanism enables the learning of local label relationships according to the label features, while the graph neural network incorporates the widely adopted ML-GCN method to guide the model in considering global label relationships simultaneously, mitigating the issue of false predictions in models caused by the limitations of label relationships. [Findings] The experimental results of MLLCLN on the public datasets MSCOCO2014 and VOC2007 demonstrate its superior performance, achieving classification accuracies of 84.4% and 96.0%, respectively. This provides a novel approach to multi-label image classification.https://tyutjournal.tyut.edu.cn/englishpaper/show-2358.htmlmulti-head self-attentionmulti-label image classificationattention mechanismadaptive weightconvolutional neural networks |
| spellingShingle | WANG Lufang ZHANG Haiyun A Multi-Label Image Classification Method based on Label Correlation Learning Network Taiyuan Ligong Daxue xuebao multi-head self-attention multi-label image classification attention mechanism adaptive weight convolutional neural networks |
| title | A Multi-Label Image Classification Method based on Label Correlation Learning Network |
| title_full | A Multi-Label Image Classification Method based on Label Correlation Learning Network |
| title_fullStr | A Multi-Label Image Classification Method based on Label Correlation Learning Network |
| title_full_unstemmed | A Multi-Label Image Classification Method based on Label Correlation Learning Network |
| title_short | A Multi-Label Image Classification Method based on Label Correlation Learning Network |
| title_sort | multi label image classification method based on label correlation learning network |
| topic | multi-head self-attention multi-label image classification attention mechanism adaptive weight convolutional neural networks |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2358.html |
| work_keys_str_mv | AT wanglufang amultilabelimageclassificationmethodbasedonlabelcorrelationlearningnetwork AT zhanghaiyun amultilabelimageclassificationmethodbasedonlabelcorrelationlearningnetwork AT wanglufang multilabelimageclassificationmethodbasedonlabelcorrelationlearningnetwork AT zhanghaiyun multilabelimageclassificationmethodbasedonlabelcorrelationlearningnetwork |