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Leveraging auxiliary text terms for automatic image annotation

Published: 28 March 2011 Publication History

Abstract

This paper proposes a novel algorithm to annotate web images by automatically aligning the images with their most relevant auxiliary text terms. First, the DOM-based web page segmentation is performed to extract images and their most relevant auxiliary text blocks. Second, automatic image clustering is used to partition the web images into a set of groups according to their visual similarity contexts, which significantly reduces the uncertainty on the relatedness between the images and their auxiliary terms. The semantics of the visually-similar images in the same cluster are then described by the same ranked list of terms which frequently co-occur in their text blocks. Finally, a relevance re-ranking process is performed over a term correlation network to further refine the ranked term list. Our experiments on a large-scale database of web pages have provided very positive results.

References

[1]
S. Bird. Nltk: The natural language toolkit. In ACL, 2006.
[2]
S. Feng, V. Lavrenko, and R. Manmatha. Multiple bernoulli relevance models for image and video annotation. In Proc. IEEE Intl. Conf. on Computer Vision and Pattern Recognition (CVPR'04), volume 2, pages 1002--1009, 2004.
[3]
B. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315:972--976, 2007.

Cited By

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  • (2015)Parallel AP Clustering and Re-ranking for Automatic Image-Text Alignment and Large-Scale Web Image SearchProceedings of the 5th ACM on International Conference on Multimedia Retrieval10.1145/2671188.2749294(451-454)Online publication date: 22-Jun-2015

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cover image ACM Other conferences
WWW '11: Proceedings of the 20th international conference companion on World wide web
March 2011
552 pages
ISBN:9781450306379
DOI:10.1145/1963192

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2011

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Author Tags

  1. automatic image annotation
  2. image-text alignment
  3. relevance re-ranking

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WWW '11
WWW '11: 20th International World Wide Web Conference
March 28 - April 1, 2011
Hyderabad, India

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2015)Parallel AP Clustering and Re-ranking for Automatic Image-Text Alignment and Large-Scale Web Image SearchProceedings of the 5th ACM on International Conference on Multimedia Retrieval10.1145/2671188.2749294(451-454)Online publication date: 22-Jun-2015

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