2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVASR-1.3
Paper Title Deep Metric Network via Heterogeneous Semantics for Image Sentiment Analysis
Authors Yun Liang, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, Hokkaido University, Japan
SessionMLR-APPL-IVASR-1: Machine learning for image and video analysis, synthesis, and retrieval 1
LocationArea D
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents a novel method for image sentiment analysis called a deep metric network via heterogeneous semantics (DMN-HS). The contribution of the proposed method is introduction of the image captioning into image sentiment analysis to reflect a global impression that cannot be represented by classical visual features extracted from images. In order to consider a sentiment correlation between visual and captioning features, the proposed method newly designs a network to integrate these heterogeneous semantics features (HS features). Furthermore, with consideration of relations among sentiments based on the HS features, the proposed method constructs a sentiment latent space by introducing the center loss concerning relationships between different sentiments and enables the classification of image sentiments. From experimental results, the performance improvement via DMN-HS is confirmed.