Web Image Annotation Using an Effective Term Weighting

  • Vundavalli Srinivasarao
  • Vasudeva Varma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7182)


The number of images on the World Wide Web has been increasing tremendously. Providing search services for images on the web has been an active research area. Web images are often surrounded by different associated texts like ALT text, surrounding text, image filename, html page title etc. Many popular internet search engines make use of these associated texts while indexing images and give higher importance to the terms present in ALT text. But, a recent study has shown that around half of the images on the web have no ALT text. So, predicting the ALT text of an image in a web page would be of great use in web image retrieval. We propose an approach on top of term co-occurrence approach proposed in the literature to ALT text prediction. Our results show that our approach and the simple term co-occurrence approach produce almost the same results. We analyze both the methods and describe the usage of the methods in different situations. We build an image annotation system on top of our proposed approach and compare the results with the image annotation system built on top of the term co-occurrence approach. Preliminary experiments on a set of 1000 images show that our proposed approach performs well over the simple term co-occurrence approach for web image annotation.


Noun Phrase Image Annotation Term Weighting Latent Dirichlet Allocation Model Anchor Text 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Vundavalli Srinivasarao
    • 1
  • Vasudeva Varma
    • 1
  1. 1.Search and Information Extraction LabInternational Institute of Information TechnologyHyderabadIndia

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