Automatic Image Annotation Based on Semi-supervised Probabilistic CCA

被引：1

作者：

Zhang, Bo ^{[1
,2
]}

Ma, Gang ^{[2
,3
]}

Yang, Xi ^{[2
]}

Shi, Zhongzhi ^{[2
]}

Hao, Jie ^{[4
]}

机构：

[1] China Univ Min & Technol, Xuzhou 221116, Peoples R China

[2] Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China

[3] Univ Chinese Acad Sci, Beijing 100190, Peoples R China

[4] Xuzhou Med Univ, Sch Med Informat, Xuzhou 221000, Peoples R China

来源：

INTELLIGENT INFORMATION PROCESSING VIII | 2016年 / 486卷

关键词：

Probabilistic CCA; Semi-supervised method; Automatic image annotation;

D O I：

10.1007/978-3-319-48390-0_22

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a novel semi-supervised method for building a statistical model that represents the relationship between images and text labels (tags) based on a semi-supervised variant of CCA called Semi-PCCA, which extends the probabilistic CCA model to make use of the labelled and unlabelled images together to extract the low-dimensional latent space representing topics of images. Real-world image tagging experiments indicate that our proposed method improves the accuracy even when only a small number of labelled images are available.

引用

页码：211 / 221

页数：11

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