원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.8 No.2
2015.02
pp.247-256
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보