一种图像检索中的灰色相关反馈算法

在交互式CBIR系统中，由于用户的查询需求常常是模糊的，因此检索结果从某种意义上说是不确定的。于是，可以将图像检索过程视为一个“灰色系统”，其中的查询向量以及图像特征的权重可视为“灰数”。基于此，该文提出了一种新的相关反馈技术，它采用“灰关联分析”理论来分析和描述“例子图像”与“相关图像”之间的关系，据此自动更新查询向量与图像特征的权重，从而更准确地描述用户的查询需求。实验结果表明，这种相关反馈算法能较好地描述用户的查询需求，显著地改善了图像检索的性能。

A Grey Relevance Feedback Algorithm for Image Retrieval

In content-based image retrieval, users query requirement may be ambiguous and subjective sometimes, and the query results are uncertain to some extent. Therefore, retrieval process can be treated as a grey system, and the query vectors and the weight values of image features as the grey numbers. This paper proposes a novel relevance feedback technique for content-based image retrieval using the grey relational analysis(GRA) method in grey system theory. The key idea of the approach is the grey relational analysis of the feature distributions of images the user has judged relevant, in order to understand what features have been taken into account (and to what extent) by the user in formulating this judgment, so that we can accentuate the influence of these features in the overall evaluation of image similarity. The method dynamically updates the query vectors and the weights for similarity measure in order to accurately represent the user's particular information needs. Experimental results show that the approach captures the users information needs more precisely.