ACM Computing Surveys (CSUR)
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A bag of paths model for measuring structural similarity in Web documents
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering web pages based on their structure
Data & Knowledge Engineering - Special issue: WIDM 2003
Visual Similarity Comparison for Web Page Retrieval
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Towards domain-independent information extraction from web tables
Proceedings of the 16th international conference on World Wide Web
Proceedings of the 16th international conference on World Wide Web
Building a Distance Function for Gestalt Grouping
IEEE Transactions on Computers
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
Table extraction using spatial reasoning on the CSS2 visual box model
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Factors affecting web page similarity
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Visually searching the web for structural content
Proceedings of the 3rd International Symposium on Visual Information Communication
VisHue: web page segmentation for an improved query interface for medlineplus medical encyclopedia
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
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Clustering and retrieval of web pages dominantly relies on analyzing either the content of individual web pages or the link structure between them. Some literature also suggests to use the structure of web pages, notably the structure of its DOM tree. However, little work considers the visual structure of web pages for clustering. In this paper (i) we motivate visual structure-based web page clustering and retrieval for a number of applications, (ii) we formalize a visual box model-based representation of web pages that supports new metrics of visual similarity, and (iii) we report on our current work on evaluating human perception of visual similarity of web pages and applying the learned visual similarity features to web page clustering and retrieval.