摘要
针对反馈固有的正负样本不对称问题和小样本问题,提出一种基于修正模糊多类SVM(FSVMs)的图像检索相关反馈算法.该算法首先将相关反馈看成一个正样本类和多个负样本类间的多分类问题,并针对原始FSVMs中模糊隶属度存在负值的情况进行了修正;然后,将受限随机选择扩展为多类情况来扩充多类负样本,并以记忆标注的方式降低用户多类标注的疲劳和误差.实验结果表明,该方法能在较少的反馈次数内得到较满意的检索结果.
In order to overcome the inherent asymmetry and the small sample size of relevance feedback （ RF）, a RF algorithm of image retrieval is proposed based on the modified multi-class fuzzy support vector machines （ FSVMs）. In this algorithm, the RF is considered as a multi-class classification problem between one relevance class and several irrelevance classes, and the original membership function of FSVMs is modified to avoid negative values. Moreover, the conventional constrained random selection method is extended to a multi-class case, and a memory marking method is used to lighten the burden of multi-class marking and to decrease the classification error. Experimental results demonstrate that the proposed algorithm helps to obtain satisfying retrieval results with less feedback times.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2008年第9期107-112,共6页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(60772122)
安徽省教育厅自然科学重点基金资助项目(KJ2007A045
KJ2008A033)
关键词
模糊多类SVM
多类受限随机选择
记忆性标注
图像检索
相关反馈
multi-class fuzzy support vector machine
multi-class constrained random selection
memory marking
image retrieval
relevance feedback
