Multidimensional Extra Evidence Mining For Image Sentiment Analysis
IEEE ACCESS(2020)
摘要
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.
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关键词
Image sentiment analysis, discriminant correlation analysis, sample-refinement, cross-modal sentimental semantics, multidimensional extra evidence mining
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