NDLI: Visual topic model for web image annotation

Content Provider	ACM Digital Library
Author	Liu, Xianming Sun, Xiaoshuai Xu, Pengfei Ji, Rongrong Tian, Qi Yao, Hongxun
Abstract	In this paper, we focus on image semantic understanding under large scale of image set, in which traditional approaches suffer from the limitations of scalability, tag correlation and noisy items. To solve these problems, a novel Visual Topic Model framework is proposed, via unsupervised clustering techniques. The framework aims at analyzing image semantics fusing both content and context, by considering tag correlations and ambiguities. In fact, the tags highly correlated in context may vary greatly in visual content and thus represent different semantics. Furthermore, a keyword selection and image annotation algorithm is also developed and applied to Flickr database with 175,770 images. Compared with the state-of-the-art methods, credible performance provides solid support for our framework.
Starting Page	126
Ending Page	130
Page Count	5
File Format	PDF
ISBN	9781450304603
DOI	10.1145/1937728.1937758
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-12-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Flickr Visual topics Keyword selection Image retrieval Image annotation Unsupervised learning
Content Type	Text
Resource Type	Article

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Visual topic model for web image annotation