Multidimensional Extra Evidence Mining for Image Sentiment Analysis

Multidimensional Extra Evidence Mining for Image Sentiment Analysis

Blog Article

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 (ME2M) for image sentiment analysis, Measuring Spoons 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 Crossbody results verify that the ME2M 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.