集成视觉特征和语义信息的相关反馈方法

为了有效地利用图像检索系统的语义分类信息和视觉特征,提出一种基于Bayes的集成视觉特征和语义信息的相关反馈检索方法.首先,将图像库的数据经语义监督的视觉特征聚类算法划分为小的聚类,每个聚类内数据的视觉特征相似并且语义类别相同;然后以聚类为单位标注正负反馈的实例,这显著区别于以单个图像为单位的相关反馈过程;最后分别以基于视觉特征的Bayes分类器和基于语义的Bayes分类器修正相似距离.在图像库上的实验表明,只用较少的反馈次数就可以达到较高的检索准确率.

Relevant Feedback Method by Integration of Visual Features and Semantics

The paper proposes a Bayes-based relevant feedback approach by integrating visual features and semantics to effectively make use of semantics and visual features in content-based image retrieval systems. First, the data of the image database are divided into small clusters by semantic supervised clustering algorithm, so the data of each cluster are similar both in visual features and in semantics. Then, on the relevant feedback, users mark the positive and negative samples, in representation of clusters instead of images. At last, we use Bayes classifiers based on visual features and based on semantics respectively to adjust retrieval similarity distance. Experimental results on an image database and a video database show that a few cycles of the relevant feedback by the proposed approach can improve the retrieval precision significantly.