원문정보
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
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