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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Editorial Office of Journal of Taiyuan University of Technology
2024-11-01
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| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2358.html |
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| Summary: | [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. |
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| ISSN: | 1007-9432 |