Earticle

초록

영어

This paper presents a new Discriminative Random Fields (DRFs) framework. Based on the DRFs framework proposed by Kumar and Hebert, the following improvements have been conducted. Firstly, the interaction potential and the associated potential model are simplified. Secondly, we reduce the dimension of the multi-scale features, re-definedimension of the single-scale feature, and increase the color feature of Building. Thirdly,the quasi-Newton method with linear search and gradient descent method are adopted to solve parameters, whichget a simple model and achieve good performance. Finally, the partition function of the DRF is eliminatedby using Pseudo-likelihood method for parameter learning. The simulation results show thatthe proposed method’s false positive rate is lower than the method from Kumar and Hebert, while the correct rate and detection ratearehigher than their experimental effects after these improvements.

목차

Abstract
 1. Introduction
 2. Image Model
  2.1. Association Potential
  2.2. Interaction Potential
  2.3. Simply Model
 3. Feature Extraction
 4. Parameter Learning
 5. Eliminate Partition Function
 6. Experiments and Discussion
  6.1. Input Image and Feature Calculation
  6.2. Experiment Results
 7. Conclusion
 Acknowledgments
 References

키워드

저자

• Yanchang Xiao [ School of Instrument Science and Engineering, Southeast University, Nanjing,China, School of Information and Engineering,China JiliangUniversity , Hangzhou,China ]
• Qing Wang [ School of Instrument Science and Engineering, Southeast University, Nanjing,China ]
• Xiaoguo Zhang [ School of Instrument Science and Engineering, Southeast University, Nanjing,China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605