基于模糊支持向量机和核方法的目标检测方法研究

Study of Object Detection Based on FSVM and Kernel Methods

介绍了模糊支持向量机(FSVM)理论,利用FSVM理论解决一般场景图像中的目标检测问题,并利用统计学习理论和支持向量机方法研究中形成的新的机器学习方法——核方法,研究FSVM的隶属度确定问题。实验表明,本算法具有较高的识别精度。本方法既具有针对性,又在理论上具有一般性,对推动模糊支持向量机这一新的模式分类方法的实际应用具有积极意义。

This thesis introduces the theory of FSVM briefly and application in an object detection system, and discusses in detail the core techniques and algorithms which determine the fuzzy memberships based on kernel methods. The algorithms takes the object detection problem as two classes classification, thus it can take the advantages of the Fuzzy SVM. The experiments show that the algorithms have good results.