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Multidimensional Extra Evidence Mining for Image Sentiment Analysis
DOI:10.1109/ACCESS.2020.2999128.png)
摘要
En 中文
Image sentiment analysis is a hot research topic in the field of computer vision. However, two key issues need to be addressed. First, high-quality training samples are scarce. There are numerous ambiguous images in the original datasets owing to diverse subjective cognitions from different annotators. Second, the cross-modal sentimental semantics among heterogeneous image features has not been fully explored. To alleviate these problems, we propose a novel model called multidimensional extra evidence mining ((MEM)-M-2) for image sentiment analysis, it involves sample-refinement and cross-modal sentimental semantics mining. A new soft voting-based sample-refinement strategy is designed to address the former problem, whereas the state-of-the-art discriminant correlation analysis (DCA) model is used to completely mine the cross-modal sentimental semantics among diverse image features. Image sentiment analysis is conducted based on the cross-modal sentimental semantics and a general classifier. The experimental results verify that the (MEM)-M-2 model is effective and robust and that it outperforms the most competitive baselines on two well-known datasets. Furthermore, it is versatile owing to its flexible structure.
Keyword:
Image sentiment analysis
discriminant correlation analysis
sample-refinement
cross-modal sentimental semantics
multidimensional extra evidence mining
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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