摘要
由于图像底层特征及其本身所包含的上层语义信息的巨大差距,使得基于内容的图像检索很难取得令人满意的效果.作为一种有效的解决方案,在过去的几年中,相关反馈在该研究领域取得了一定的成功.提出了一种新的具有学习能力的反馈算法.该算法基于贝叶斯分类原理,运用不同的反馈策略分别处理正、负反馈,同时它具有学习能力,可以运用用户的反馈信息不断地修正检索参数,使系统的检索能力得到不断的提高.通过在大图片库上的检索实验,该算法产生的效果大大优于当前其他的反馈方法.
The biggest problem in content-based image retrieval (CBIR) is a big gap between high-level semantic contents and low-level features. As an effective solution, relevance feedback has been put on many efforts for the past few years. In this paper, a new relevance feedback approach with progressive learning capability is proposed. It is based on a Bayesian classifier and treats positive and negative feedback examples with different strategies. It can utilize previous users?feedback information to improve its retrieval ability. The experimental results show that this algorithm achieves high accuracy and effectiveness on real-world image collections.
出处
《软件学报》
EI
CSCD
北大核心
2002年第10期2001-2006,共6页
Journal of Software
基金
国家自然科学基金资助项目(69823001)
国家重点基础研究发展规划973资助项目(G1998030509)~
