基于索引和相关反馈的图像检索研究

在基于图像内容的图像检索(CBIR)系统中,搜索引擎检索图像类似于按照相似标准来查询图像,它应该有足够快的速度并且有较高的检索准确率.索引用来提高系统响应,而相关反馈用于帮助提高检索准确率.在本文中,主要说明基于人感知的相似性度量,以及讨论综合相关反馈的索引方案.该索引方案通过分析特征熵而得出的主从键,而相关反馈是根据Mann-Whitnev检验而提出的,该检验通常用来识别来自同一搜索集中相关图像和不相关图像之间不同特征,并利用不同特征的特点提高检索性能.相关反馈方案针对两不同相似标准来执行,检验判定了这个方法的有效性.最后,把索引机制和相关反馈机制结合起来建立搜索引擎.

Research on Indexing and Relevance Feedback Based on Image Retrieval

In content based image retrieval (CBIR) system, search engine retrieves the images similar standard to the query image according to a similarity measure. It should be fast enough and have a high precision of retrieval. Indexing scheme is used to achieve a fast response and relevance feedback helps in improving the retrieval precision. In this paper, a human perception based similarity measure is presented and based on it a simple yet novel indexing scheme with relevance feedback is discussed. The indexing scheme is designed based on the primary and secondary keys which are selected by analysing the entropy of features. A relevance feedback method is proposed based on Mann-Whitney test. The test is used to identify the discriminating features from the relevant and irrelevant images in a retrieved set. Then emphasis of the discriminating features are updated to improve the retrieval performance. The relevance feedback scheme is implemented for two different similarity measure (Euclidean distance based and human perception based). The experiment justifies the effectiveness of the proposed methodologies. Finally,the indexing scheme and relevance feedback mechanism are combined to build up the search engine.