| Skip to main content | Skip to navigation |

Register Now!

Browsing on Small Screens: Recasting Web-Page Segmentation into an Efficient Machine Learning Framework

  • Shumeet Baluja, Google, Inc., USA

Full text:

Track: Pervasive Web and Mobility

Fitting enough information from webpages to make browsing on small screens compelling is a challenging task. One approach is to present the user with a thumbnail image of the full web page and allow the user to simply press a single key to zoom into a region (which may then be transcoded into wml/xhtml, summarized, etc). However, if regions for zooming are presented naively, this yields a frustrating experience because of the number of coherent regions, sentences, images, and words that may be inadvertently separated. Here, we cast the web page segmentation problem into a machine learning framework, where we re-examine this task through the lens of entropy reduction and decision tree learning. This yields an efficient and effective page segmentation algorithm. We demonstrate how simple techniques from computer vision can be used to fine-tune the results. The resulting segmentation keeps coherent regions together when tested on a broad set of complex webpages.

Citation

Baluja, S. 2006. Browsing on small screens: recasting web-page segmentation into an efficient machine learning framework. In Proceedings of the 15th International Conference on World Wide Web (Edinburgh, Scotland, May 23 - 26, 2006). WWW '06. ACM Press, New York, NY, 33-42.
DOI= http://doi.acm.org/10.1145/1135777.1135788

Organised by

ECS Logo

in association with

BCS Logo ACM Logo

Platinum Sponsors

Sponsor of The CIO Dinner


Become a sponsor or exhibitor
Valid XHTML 1.0! IFIP logo WWW Conference Committee logo Web Consortium logo Valid CSS!