Earticle

초록

영어

A semantic block is treated as a unit while analyzing the webpage. First, we implement the VTPS algorithm to partition a webpage into semantic blocks. Then, we propose an algorithm to extract the spatial and content features, and then construct the feature vector for each block. Based on these vectors, the SVM learning algorithm is applied to train and classify the various theme-oriented webpage blocks. At last, the classification experiments show the efficiency of this method.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Document Object Model Structure
  2.2. Page Segmentations based on Layout
  2.3. Block Importance Model
 3. Vision-based Webpage Segmentation Principle
 4. Vision-based Theme-oriented Webpage Partition Model
  4.1. Extracting Text features of Blocks
  4.2. Extracting Visual Features of Blocks
 5. Model Measurement and Analysis
  5.1. Evaluation Standard of Model
  5.2. Testing and Analysis
  5.3. Application of Webpage Partition Model
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Lv Fang [ Harbin Institute of Technology at Weihai, Shandong, 264209 ]
• Huang Junheng [ Harbin Institute of Technology at Weihai, Shandong, 264209 ]
• Wei Yuliang [ Harbin Institute of Technology at Weihai, Shandong, 264209 ]
• Wang Bailing [ Harbin Institute of Technology at Weihai, Shandong, 264209 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605