Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-92892-8_3
Title: Adaptive model for integrating different types of associated texts for automated annotation of Web images
Authors: Xu, H.
Zhou, X.
Lin, L.
Wang, M.
Chua, T.-S. 
Keywords: Adaptive model
Image annotation
Image content analysis
Issue Date: 2009
Source: Xu, H.,Zhou, X.,Lin, L.,Wang, M.,Chua, T.-S. (2009). Adaptive model for integrating different types of associated texts for automated annotation of Web images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5371 LNCS : 3-14. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-92892-8_3
Abstract: A lot of texts are associated with Web images, such as image file name, ALT texts, surrounding texts etc on the corresponding Web pages. It is well known that the semantics of Web images are well correlated with these associated texts, and thus they can be used to infer the semantics of Web images. However, different types of associated texts may play different roles in deriving the semantics of Web contents. Most previous work either regard the associated texts as a whole, or assign fixed weights to different types of associated texts according to some prior knowledge or heuristics. In this paper, we propose a novel linear basic expansion-based approach to automatically annotate Web images based on their associated texts. In particular, we adaptively model the semantic contributions of different types of associated texts by using a piecewise penalty weighted regression model. We also demonstrate that we can leverage the social tagging data of Web images, such as the Flickr's Related Tags, to enhance the performance of Web image annotation. Experiments conducted on a real Web image data set demonstrate that our approach can significantly improve the performance of Web image annotation. © 2008 Springer Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41811
ISBN: 354092891X
ISSN: 03029743
DOI: 10.1007/978-3-540-92892-8_3
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