期刊论文

Li, Zhixin(lizx@gxnu.edu.cn)

英文

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS

1551-6857

2021

17

1

37:1-37:23

10.1145/3426974

This work is supported by the National Natural Science Foundation of China (Nos. 61966004, 61663004, 61762078, 61866004, 61876111), the Guangxi Natural Science Foundation (Nos. 2019GXNSFDA245018, 2018GXNSFDA281009), the Guangxi “Bagui Scholar” Teams for Innovation and Research Project, the Guangxi Talent Highland Project of Big Data Intelligence and Application, and Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing. Authors’ addresses: Z. Li (corresponding author), L. Lin, and C. Zhang, Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, No.15 Yucai Rd of Qixing District, Guilin, Guangxi, 541004, China; emails: lizx@gxnu.edu.cn, linlan_2015@sina.com, clzhang@gxnu.edu.cn; H. Ma, College of Computer Science and Engineering, Northwest Normal University, No.967 Anning East Rd of Anning District, Lanzhou, Gansu, 730070, China; email: mahuifang@yeah.net; W. Zhao, School of Computer, Central China Normal University, No.152 Luoyu Rd of Hongshan District, Wuhan, Hubei, 430079, China; email: zhaowz81@163.com; Z. Shi, College of Information Engineering, Capital Normal University, No.105 West Third Ring North Rd of Haidian District, Beijing, 100048, China; email: shizp@cnu.edu.cn. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Association for Computing Machinery. 1551-6857/2021/04-ART37 $15.00 https://doi.org/10.1145/3426974

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To learn a well-performed image annotation model, a large number of labeled samples are usually required. Although the unlabeled samples are readily available and abundant, it is a difficult task for humans to annotate large numbers of images manually. In this article, we propose a novel semi-supervised approach based on adaptive weighted fusion for automatic image annotation that can simultaneously utilize the labeled data and unlabeled data to improve the annotation performance. At first, two different classifiers, constructed based on suppor...