2021年
Deep Metric Network Via Heterogeneous Semantics for Image Sentiment Analysis.
ICIP
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- 開始ページ
- 1039
- 終了ページ
- 1043
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICIP42928.2021.9506701
- 出版者・発行元
- IEEE
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.
- リンク情報
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- DOI
- https://doi.org/10.1109/ICIP42928.2021.9506701
- DBLP
- https://dblp.uni-trier.de/rec/conf/icip/0014M0H21
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000819455101033&DestApp=WOS_CPL
- URL
- https://dblp.uni-trier.de/rec/conf/icip/2021
- URL
- https://dblp.uni-trier.de/db/conf/icip/icip2021.html#0014M0H21
- ID情報
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- DOI : 10.1109/ICIP42928.2021.9506701
- ISSN : 1522-4880
- ISBN : 9781665431026
- ISBN : 9781665441155
- DBLP ID : conf/icip/0014M0H21
- Web of Science ID : WOS:000819455101033