Image interpretation： mining the visible and syntactic correlation of annotated words

Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to ren...

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Bibliographic Details
Published in:Journal of Zhejiang University. A. Science 2009-12, Vol.10 (12), p.1759-1768
Main Authors: Xia, Ding-yin, Wu, Fei, Liu, Wen-hao, Zhang, Han-wang
Format: Article
Language:English
Subjects:
Civil Engineering
Classical and Continuum Physics
Engineering
Industrial Chemistry/Chemical Engineering
Mechanical Engineering
图像理解
图像解译
文字信息
目标图像
自然语言
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Summary:Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as ＂pandas eat bamboo＂. In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts： first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.
ISSN:1673-565X
1862-1775
DOI:10.1631/jzus.A0820856