Visual topic model for web image annotation

Xianming Liu, Hongxun Yao, Rongrong Ji, Pengfei Xu, Xiaoshuai Sun, Qi Tian
<span title="">2010</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/c3hdogiqszdadoxykcz3v6sgn4" style="color: black;">Proceedings of the Second International Conference on Internet Multimedia Computing and Service - ICIMCS &#39;10</a> </i> &nbsp;
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
more &raquo; ... eatly 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1937728.1937758">doi:10.1145/1937728.1937758</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icimcs/LiuYJXST10.html">dblp:conf/icimcs/LiuYJXST10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/deu7gz4xzndxppasmaxgzygt6y">fatcat:deu7gz4xzndxppasmaxgzygt6y</a> </span>
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