Image Emotional Semantic Annotation Based on Fusion Features

被引：0
|
作者：
Zhang, Xuliang [1 ]
Lou, Sudi [2 ]
机构：
[1] Shenzhen Polytech, Shengzheng, Peoples R China
[2] Hangzhou Dianzi Univ, Hangzhou, Zhejiang, Peoples R China
来源：
2017 10TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI) | 2017年
关键词：
component; image; emotion semantic annotation; fusion features; least squares support vector machine;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Due to "semantic gap", the problem of image emotional semantic annotation has not been solved. In this parper, a method of emotion semantic annotation for cheongsam images based on Fusion Features has been proposed. Multi-features including the color and texture are used to describe the content of the image. Then least squares support vector machine for regression which is optimized by particle swarm optimization is used to build the mapping between the feature space and emotional space. The experiment indicates that this method achieves good effect.
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页数：5
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