摘要
融合基于区域的图像表达方式和相关反馈技术能够有效地提高图像检索的性能。由于现有的方法没有充分考虑相同语义类内区域特征的分布情况,进而无法对该类的语义信息进行有效的描述,为此该文提出了贝叶斯框架下基于区域的相关反馈模型。在每轮相关反馈中,通过在线学习区域的贝叶斯分类器,同时根据最近邻最小错误估计原则确定分类器的可信度,可以可靠地建立图像相似性度量的概率模型。此外,在应用非参数密度估计技术来构造语义类的特征分布时,针对区域分割的不精确性,该文还考虑了区域特征空间的总体分布因素,进而对区域的后验分布进行更可靠地估计。最后的实验说明了该文方法的有效性。
Many researchers have found it can improve the retrieval performance by combining region-based representation and relevance feedback technology. Since the previous works have ignored the probabilistic distribution of regions in the same semantic class, it is hard to represent the semantic information effectively. In this paper, Bayesian probabilistic model based on region and relevance feedback is proposed. The probability model of image similarity can be constructed via the Bayesian classifier obtained by on-line learning and its certainty based on the least error probability of the nearest region in relevant images set. When it comes to the non-parameter density estimation technique for characterizing the region feature distribution, it also takes the collective distribution into consideration because of inaccurate segmentation. Thus, the posterior distribution of region feature can be estimated accurately, and the experimental results demonstrate its effectiveness.
出处
《电子与信息学报》
EI
CSCD
北大核心
2008年第4期937-940,共4页
Journal of Electronics & Information Technology
基金
国家973计划(2006-B30314)
国家自然科学基金(90604032，0602030)
新世纪优秀人才支持计划
NLPR国家重点实验室开放基金
视觉与听觉信息处理国家重点实验室开放基金资助课题
关键词
相关反馈
贝叶斯分类器
非参数密度估计
Relevance feedback
Bayesian classifier
Non-parametric density estimation
