Abstract
In image annotation, the annotation words are expected to represent image content at both visual level and semantic level. However, a single word sometimes is ambiguous in annotation, for example, ”apple” may refer to a fruit or a company. However, when ”apple” combines with ”phone” or ”fruit”, it will be more semantically and visually consistent. In this paper, we attempt to find this kind of combination and construct a less ambiguous phrase-level lexicon for annotation. First, concept-based image search is conducted to obtain a semantically consistent image set (SC-IS). Then, a hierarchical clustering algorithm is adopted to visually cluster the images in SC-IS to obtain a semantically and visually specific image set (SVC-IS). Finally, we apply a frequent itemset mining in SVC-IS to construct the phrase-level lexicon and associate the lexicon into a probabilistic annotation framework to estimate annotation words of any untagged images. Our experimental results show that the discovered phrase-level lexicon is able to improve the annotation performance.
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Yu, L., Liu, J., Xu, C. (2010). Discovering Phrase-Level Lexicon for Image Annotation. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15702-8_17
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DOI: https://doi.org/10.1007/978-3-642-15702-8_17
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