A Correlation Approach for Automatic Image Annotation

Hardoon, David R.; Saunders, Craig; Szedmák, Sándor; Shawe‐Taylor, John

doi:10.1007/11811305_75

Cited by 36 publications

(27 citation statements)

References 10 publications

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“…the debut of CCA, a multitude of analyses, adaptations and applications have been proposed (Ketterling 1971;Akaho 2001;Friman et al 2001aFriman et al , 2001bBach and Jordan 2002;Hardoon et al 2004Hardoon et al , 2006Fukumizu et al 2007;Szedmak et al 2007).…”

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confidence: 99%

Sparse canonical correlation analysis

2010

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We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view. Sparse CCA (SCCA) minimises the number of features used in both the primal and dual projections while maximising the correlation between the two views. The method is compared to alternative sparse solutions as well as demonstrated on paired corpuses for mate-retrieval. We are able to observe, in the mate-retrieval, that when the number of the original features is large SCCA outperforms Kernel CCA (KCCA), learning the common semantic space from a sparse set of features.

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confidence: 99%

Sparse canonical correlation analysis

2010

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“…Another way to deal with the case when the tags are available for only a small number of webpages is to use the tagged webpages for predicting the tags for the rest of them (akin to the framework proposed by [Hardoon et al 2006] which automatically annotates images using annotations for similar images). Under this approach, one can perform a latent semantic analysis or CCA to discover a semantic subspace of webpages having tag information available.…”

Section: Partially Tagged Corpusmentioning

confidence: 99%

Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering

Trivedi

Rai

Daumé

et al. 2012

ACM Trans. Intell. Syst. Technol.

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Automatic clustering of webpages helps a number of information retrieval tasks, such as improving user interfaces, collection clustering, introducing diversity in search results, etc. Typically, webpage clustering algorithms only use features extracted from the page-text. However, the advent of social-bookmarking websites, such as StumbleUpon.com and Delicious.com, has led to a huge amount of user-generated content such as the social tag information that is associated with the webpages. In this paper, we present a subspace based feature extraction approach which leverages the social tag information to complement the page-contents of a webpage for extracting beter features, with the goal of improved clustering performance. In our approach, we consider page-text and tags as two separate views of the data, and learn a shared subspace that maximizes the correlation between the two views. Any clustering algorithm can then be applied in this subspace. We then present an extension that allows our approach to be applicable even if the webpage corpus is only partially tagged, i.e., when the social tags are present for not all, but only for a small number of webpages. We compare our subspace based approach with a number of baselines that use tag information in various other ways, and show that the subspace based approach leads to improved performance on the webpage clustering task. We also discuss some possible future work including an active learning extension that can help in choosing which webpages to get tags for, if we only can get the social tags for only a small number of webpages.

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“…Another way to deal with the case when the tags are available for only a small number of webpages is to use the tagged webpages for predicting the tags for the rest of them (akin to the framework proposed by [20] which automatically annotates images using annotations for similar images). Under this approach, one can perform a latent semantic analysis or CCA to discover a semantic subspace of webpages having tag information available.…”

Section: Predicting Tags For Non-tagged Webpagesmentioning

confidence: 99%

Exploiting tag and word correlations for improved webpage clustering

Trivedi

Rai

DuVall

et al. 2010

Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents

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Automatic clustering of webpages helps a number of information retrieval tasks, such as improving user interfaces, collection clustering, introducing diversity in search results, etc. Typically, webpage clustering algorithms only use features extracted from the page-text. However, the advent of social-bookmarking websites, such as StumbleUpon 1 and Delicious 2 , has led to a huge amount of user-generated content such as the tag information that is associated with the webpages. In this paper, we present a subspace based feature extraction approach which leverages tag information to complement the page-contents of a webpage to extract highly discriminative features, with the goal of improved clustering performance. In our approach, we consider page-text and tags as two separate views of the data, and learn a shared subspace that maximizes the correlation between the two views. Any clustering algorithm can then be applied in this subspace. We compare our subspace based approach with a number of baselines that use tag information in various other ways, and show that the subspace based approach leads to improved performance on the webpage clustering task. Although our results here are on the webpage clustering task, the same approach can be used for webpage classification as well. In the end, we also suggest possible future work for leveraging tag information in webpage clustering, especially when tag information is present for not all, but only for a small number of webpages. * Also holds an adjunct position with the

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A Correlation Approach for Automatic Image Annotation

Cited by 36 publications

References 10 publications

Sparse canonical correlation analysis

Sparse canonical correlation analysis

Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering

Exploiting tag and word correlations for improved webpage clustering

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