Learning Image-Text Associations
Tao Jiang and Ah-Hwee Tan, Senior Member, IEEE
Abstract—Web information fusion can be deﬁned as the problem of collating and tracking information related to speciﬁc topics on the World Wide Web. Whereas most existing work on web information fusion has focused on text-based multi-document summarization, this paper concerns the topic of image and text association, a cornerstone of cross-media web information fusion. Speciﬁcally, we present two learning methods for discovering the underlying associations between images and texts based on small training data sets. The ﬁrst method based on vague transformation measures the information similarity between the visual features and the textual features through a set of predeﬁned domain-speciﬁc information categories. Another method uses a neural network to learn direct mapping between the visual and textual features by automatically and incrementally summarizing the associated features into a set of information templates. Despite their distinct approaches, our experimental results on a terrorist domain document set show that both methods are capable of learning associations between images and texts from a small training data set. Index Terms—Data Mining, Multimedia Data Mining, Image-Text Association Mining.
The diverse and distributed nature of the information published on the World Wide Web has made it difﬁcult to collate and track information related to speciﬁc topics. Although web search engines have reduced information overloading to a certain extent, the information in the retrieved documents still contains a lot of redundancy. Techniques are needed in web information fusion, involving ﬁltering of irrelevant and redundant information, collating of information according to themes, and generation of coherent presentation. As a commonly used technique for information fusion, document summarization... [continues]
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