查询子空间在图像检索中的应用

近几年来,为了解决图像检索系统中由底层视觉特征和高层语义之间的差异所造成的检索困难,从信息捡索中引入了相关反馈技术。在过去几年中,它在该研究领域取得了一定的成功。文章提出了一种利用反馈信息建立“查询子空间”的检索模型,它将用户的语义查询进行基于视觉特征的分类,构造多个“查询子空间”,这些子空间拥有自身的查询模型和检索模型,最后的检索结果根据这多个“查询子空间”的检索结果得到。该模型具有较强的灵活性、扩展性,有效地利用了用户的反馈信息,动态地建立了底层视觉特征和高层语义之间的映射,能适应不同用户的查询。同时,将负反馈信息合理地融入到该模型中,提高了系统的检索效率。实验结果表明采用该检索模型的系统相比现有的检索系统性能有了较大提高。

The Application of Query Subspaces in Content-based Image Retrieval

In the past few years,as an effective solution for the gap between low-level visual feature and high-level semantics,relevance feedback (RF)has been introduced into image retrieval systems.This paper proposes a novel"query subspace"-based retrieval model,that firstly classifies the positive images provided by user,and then uses these classes of samples to form several query subspaces,which have their own query and retrieval model.The ultimate retrieval result is based on the retrieval results of these query subspaces.The method dynamically builds the mapping between low-level visual and high -level semantics and adapts to different queries.At the same time, it rationally integrates the negative information into this model to improve the efficiency of system.The experimental results show that the proposed method is able to greatly improve the efficiency of image retrieval system.