research-article

Web page segmentation evaluation

Authors:

Andrés Sanoja,

Stéphane GançarskiAuthors Info & Claims

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

Pages 753 - 760

https://doi.org/10.1145/2695664.2695786

Published: 13 April 2015 Publication History

Abstract

In this paper, we present a framework for evaluating segmentation algorithms for Web pages. Web page segmentation consists in dividing a Web page into coherent fragments, called blocks. Each block represents one distinct information element in the page. We define an evaluation model that includes different metrics to evaluate the quality of a segmentation obtained with a given algorithm. Those metrics compute the distance between the obtained segmentation and a manually built segmentation that serves as a ground truth. We apply our framework to four state-of-the-art segmentation algorithms (BOM, Block Fusion, VIPS and JVIPS) on several categories (types) of Web pages. Results show that the tested algorithms usually perform rather well for text extraction, but may have serious problems for the extraction of geometry. They also show that the relative quality of a segmentation algorithm depends on the category of the segmented page.

References

[1]

Abiteboul, S.: Querying semi-structured data. In: Afrati, F. N., Kolaitis, P. G. (eds.) Database Theory - ICDT '97, 6th International Conference, Delphi, Greece, January 8-10, 1997, Proceedings. Lecture Notes in Computer Science, vol. 1186, pp. 1--18. Springer (1997)

Digital Library

[2]

Asakawa, C., Takagi, H.: Annotation-based transcoding for nonvisual web access. In: Proceedings of the Fourth International ACM Conference on Assistive Technologies. pp. 172--179. Assets '00, ACM, New York, NY, USA (2000), http://doi.acm.org/10.1145/354324.354588

Digital Library

[3]

Baluja, S.: 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. pp. 33--42. ACM (2006)

Digital Library

[4]

Breuel, T. M.: Representations and metrics for off-line handwriting segmentation. In: Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop on. pp. 428--433. IEEE (2002)

Digital Library

[5]

Cai, D., Yu, S., Wen, J. R., Ma, W. Y.: Extracting content structure for web pages based on visual representation. In: APWeb 2003. LNCS, vol. 2642, pp. 406--417. Springer (2003)

Digital Library

[6]

Cattoni, R., Coianiz, T., Messelodi, S., Modena, C.: Geometric layout analysis techniques for document image understanding: a review. ITC-irst Technical Report 9703(09) (1998)

[7]

Chakrabarti, D., Kumar, R., Punera, K.: A graph-theoretic approach to webpage segmentation. In: Proceedings of the 17th international conference on World Wide Web. pp. 377--386. ACM (2008)

Digital Library

[8]

Chen, Y., Xie, X., Ma, W. Y., Zhang, H. J.: Adapting web pages for small-screen devices. IEEE Internet Computing 9(1), 50--56 (2005)

Digital Library

[9]

Hu, J., Kashi, R., Wilfong, G.: Document image layout comparison and classification. In: 1999. ICDAR '99. Proceedings of the Fifth International Conference on Document Analysis and Recognition. pp. 285--288 (Sep 1999)

Digital Library

[10]

Kohlschütter, C., Nejdl, W.: A densitometric approach to web page segmentation. In: Proceedings of the 17th ACM conference on Information and knowledge management. pp. 1173--1182. ACM (2008)

Digital Library

[11]

Kreuzer, R.: A Quantitative Comparison of Semantic Web Page Segmentation Algorithms. Master's thesis, Universiteit Utrecht (2013)

[12]

Pehlivan, Z., Saad, M. B., Gançarski, S.: Vi-diff: Understanding web pages changes. In: DEXA (1). pp. 1--15 (2010)

Digital Library

[13]

Popela, T.: IMPLEMENTACE ALGORITMU PRO VIZUALNI SEGMENTACI WWW STRANEK. Master's thesis, BRNO University of Technology (2012)

[14]

Saad, M. B., Gançarski, S.: Using visual pages analysis for optimizing web archiving. In: Proceedings of the 2010 EDBT/ICDT Workshops. p. 43. ACM (2010)

Digital Library

[15]

Saad, M. B., Gançarski, S.: Archiving the web using page changes patterns: a case study. Int. J. on Digital Libraries 13(1), 33--49 (2012)

Digital Library

[16]

Sanoja, A., Gançarski, S.: Block-o-matic: A web page segmentation framework. In: International Conference on Multimedia Computing and Systems (ICMCS'14). Marrakeh, Morroco (2014)

[17]

Shafait, F., Keysers, D., Breuel, T.: Performance evaluation and benchmarking of six-page segmentation algorithms. Pattern Analysis and Machine Intelligence, IEEE Transactions on 30(6), 941--954 (2008)

Digital Library

[18]

Solis, B.: The conversation prism (2014), https://conversationprism.com/

[19]

Tang, Y. Y., Suen, C. Y.: Document structures: a survey. International journal of pattern recognition and artificial intelligence 8(05), 1081--1111 (1994)

[20]

Xiao, Y., Tao, Y., Li, Q.: Web page adaptation for mobile device. In: Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on. pp. 1--5 (2008)

[21]

Yesilada, Y.: Web page segmentation: A review. Tech. rep., University of Manchester and Middle East Technical University Northern Cyprus Campus (2011)

[22]

Zhang, Y., Gerbrands, J.: Objective and quantitative segmentation evaluation and comparison. Signal processing 39(1), 43--54 (1994)

Digital Library

Cited By

Griazev K(2024)Internet Web page content block dataset and solutions for its data labelling simplificationundefined10.20334/2024-032-MOnline publication date: 2024
https://doi.org/10.20334/2024-032-M
Griazev KRamanauskaitė S(2023)Web Page Content Block Identification with Extended Block PropertiesApplied Sciences10.3390/app1309568013:9(5680)Online publication date: 5-May-2023
https://doi.org/10.3390/app13095680
Jayashree SDias GAndrew JSaha SMaurel FFerrari S(2022)Multimodal Web Page Segmentation Using Self-organized Multi-objective ClusteringACM Transactions on Information Systems10.1145/348096640:3(1-49)Online publication date: 7-Mar-2022
https://dl.acm.org/doi/10.1145/3480966
Show More Cited By

Index Terms

Web page segmentation evaluation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation
2. Information systems
  1. Information retrieval
  2. Information storage systems

Recommendations

Web Page Segmentation Revisited: Evaluation Framework and Dataset
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Each web page can be segmented into semantically coherent units that fulfill specific purposes. Though the task of automatic web page segmentation was introduced two decades ago, along with several applications in web content analysis, its foundations ...
Unsupervised segmentation evaluation: an edge-based method

Unsupervised segmentation evaluation method quantifies the quality of segmentation without the reference segmentation or user assistance. Although some methods have been proposed to statistically analyze the pixel values, these methods are not sensitive ...
Evaluation method for MRI brain tissue abnormalities segmentation study
Proceedings of the 15th WSEAS international conference on Computers

Segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research as it can facilitates the neurological diseases diagnosis. However, there ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

April 2015

2418 pages

ISBN:9781450331968

DOI:10.1145/2695664

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Juan Manuel Corchado
University of Salamanca, Spain
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Funding Sources

FP7 ICT-2009.4.1

Conference

SAC 2015

Sponsor:

SIGAPP

SAC 2015: Symposium on Applied Computing

April 13 - 17, 2015

Salamanca, Spain

Acceptance Rates

SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
154
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)2

Reflects downloads up to 20 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Griazev K(2024)Internet Web page content block dataset and solutions for its data labelling simplificationundefined10.20334/2024-032-MOnline publication date: 2024
https://doi.org/10.20334/2024-032-M
Griazev KRamanauskaitė S(2023)Web Page Content Block Identification with Extended Block PropertiesApplied Sciences10.3390/app1309568013:9(5680)Online publication date: 5-May-2023
https://doi.org/10.3390/app13095680
Jayashree SDias GAndrew JSaha SMaurel FFerrari S(2022)Multimodal Web Page Segmentation Using Self-organized Multi-objective ClusteringACM Transactions on Information Systems10.1145/348096640:3(1-49)Online publication date: 7-Mar-2022
https://dl.acm.org/doi/10.1145/3480966
Kiesel JKneist FMeyer LKomlossy KStein BPotthast Md'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Web Page Segmentation RevisitedProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412782(3047-3054)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412782
Andrew JFerrari SMaurel FDias GGiguet E(2020)Model-Driven Web Page Segmentation for Non Visual AccessComputational Linguistics10.1007/978-981-15-6168-9_17(191-205)Online publication date: 2-Jul-2020
https://doi.org/10.1007/978-981-15-6168-9_17
Blustein JDi Matteo NMacrini DAtzenbeck CRubart J(2019)Designing Experiments to Compare Web Page SegmentersProceedings of the 2nd International Workshop on Human Factors in Hypertext10.1145/3345509.3349280(27-29)Online publication date: 12-Sep-2019
https://dl.acm.org/doi/10.1145/3345509.3349280
Malhotra PMalik S(2018)Web Page Segmentation Towards Information Extraction for Web SemanticsInternational Conference on Innovative Computing and Communications10.1007/978-981-13-2354-6_45(431-442)Online publication date: 20-Nov-2018
https://doi.org/10.1007/978-981-13-2354-6_45
Sanoja AGançarski S(2017)Migrating Web Archives from HTML4 to HTML5: A Block-Based Approach and Its EvaluationAdvances in Databases and Information Systems10.1007/978-3-319-66917-5_25(375-393)Online publication date: 25-Aug-2017
https://doi.org/10.1007/978-3-319-66917-5_25

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents