标题:CoDS: Co-training with Domain Similarity for Cross-Domain Image Sentiment Classification
作者:Zhang, Linlin; Chen, Meng; Yu, Xiaohui; Liu, Yang
通讯作者:Yu, Xiaohui
作者机构:[Zhang, Linlin; Chen, Meng; Yu, Xiaohui; Liu, Yang] Shandong Univ, Sch Comp Sci & Technol, Jinan, Peoples R China.; [Yu, Xiaohui] York Univ, Sch Inf 更多
会议名称:18th Asia-Pacific Web Conference (APWeb)
会议日期:SEP 23-25, 2016
来源:Web Technologies and Applications, Pt I
出版年:2016
卷:9931
页码:480-492
DOI:10.1007/978-3-319-45814-4_39
摘要:Classifying images according to the sentiments expressed therein has a wide range of applications, such as sentiment-based search or recommendation. Most existing methods for image sentiment classification approach this problem by training general classifiers based on certain visual features, ignoring the discrepancies across domains. In this paper, we propose a novel co-training method with domain similarity (CoDS) for cross-domain image sentiment classification in social applications. The key idea underlying our approach is to use both the images and the corresponding textual comments when training classifiers, and to use the labeled data of one domain to make sentiment classification for the images of another domain through co-training. We compute image/text similarity between the source domain and the target domain and set the weighting of the corresponding classifiers to improve performance. We perform extensive experiments on a real dataset collected from Flickr. The experimental results show that our proposed method significantly outperforms the baseline methods.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989298029&doi=10.1007%2f978-3-319-45814-4_39&partnerID=40&md5=d50b8cdecc554c5082b1db910901e013
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