iFind: 一个结合语义和视觉特征的图像相关反馈检索系统

给出了一个结合语义与视觉特征信息的图像相关反馈检索系统－－iFind。系统通过图像的标注信息构造语义网络，并在相关反馈中与图像的视觉特征相结合，有效地实现了在两个层次上的相关反馈，在基于内容的图像检索中取得了较为理想的效果，具有一定的应用价值。

iFind: An Image Retrieval System with Relevance Feedback Based on the Combination of Semantics and Visual Features

The relevance feedback approach to image retrieval is a powerful technique and has been an active research direction for the past few years. Various ad hoc parameter estimation techniques have been proposed for relevance feedback. In addition, methods that perform optimization on multi level image content model have been formulated. However, these methods only perform relevance feedback on the low level image features and fail to address the images' semantic content. This paper proposes a relevance feedback technique, iFind, to take advantage of the semantic contents of the images in addition to the low level features. By forming a semantic network on top of the keyword association on the images, it is able to accurately deduce and utilize the images' semantic contents for retrieval purposes. The experimental results on real world image collections demonstrate accuracy and effectiveness of the method.