摘要: 为了提高图像检索的效率,近年来相关反馈机制被引入到基于内容的图像检索领域,而在基于内容的图像检索系统中,多特征融合检索中的特征加权又是一个重要的问题。该文提出了一种新的基于特征加权的相关反馈方法,在粗集理论的基础上,结合用户标记的反馈图像建立决策表,通过决策规则的精度来对多个特征加权,使图像检索和人的感知更加接近。实验表明该方法是有效的,并较Rui的相关反馈方法在性能上有很大提高。
关键词:
CBIR,
粗糙集,
相关反馈
Abstract: In the past few years, content-based image retrieval has been becoming an active research area. There exists a gap between high-level concepts and low-level features. Relevance feedback is a promising approach to finding a mapping between semantic objects and low-level features. Feature weighting is also an important issue of multiple features combination in content-based image retrieval. This paper proposes a feature-weighting scheme based on rough set in relevance feedback. During the feedback process, a decision table is constructed. Then the weight of a feature space is determined by the precision of the decision rules. The experiments show that this approach is effective in feature weighting for content-based image retrieval, which gets higher efficiency than Rui’s algorithm.
Key words:
CBIR,
Rough set,
Relevance feedback
中图分类号:
&#;冯 林;袁 彬;孙 焘 ;滕弘飞;. 图像检索中基于粗集理论的特征加权方法[J]. 计算机工程, 2006, 32(18): 208-210.
FENG Lin; YUAN Bin; SUN Tao ; TENG Hongfei;. Rough Set Feature Weighting Method for Image Retrieval[J]. Computer Engineering, 2006, 32(18): 208-210.