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CLC number: TP391

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Received: 2005-08-05

Revision Accepted: 2005-09-10

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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.11 P.1268-1283


Exploiting multi-context analysis in semantic image classification

Author(s):  TIAN Yong-hong, HUANG Tie-jun, GAO Wen

Affiliation(s):  Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China; more

Corresponding email(s):   yhtian@ict.ac.cn, tjhuang@ict.ac.cn, wgao@ict.ac.cn

Key Words:  Image classification, Multi-context analysis, Cross-modal correlation analysis, Link-based correlation model, Linkage semantic kernels, Relational support vector classifier

TIAN Yong-hong, HUANG Tie-jun, GAO Wen. Exploiting multi-context analysis in semantic image classification[J]. Journal of Zhejiang University Science A, 2005, 6(11): 1268-1283.

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author="TIAN Yong-hong, HUANG Tie-jun, GAO Wen",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Exploiting multi-context analysis in semantic image classification
%A TIAN Yong-hong
%A HUANG Tie-jun
%A GAO Wen
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 11
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%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A1268

T1 - Exploiting multi-context analysis in semantic image classification
A1 - TIAN Yong-hong
A1 - HUANG Tie-jun
A1 - GAO Wen
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 11
SP - 1268
EP - 1283
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A1268

As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification approach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based correlation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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