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. In this paper, we propose 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, we are able to accurately deduce and utilize the images' semantic contents for retrieval purposes. The accuracy and effectiveness of our method is demonstrated with experimental results on real-world image collections.
机译:基于内容的图像检索的相关反馈的卷积神经网络基于内容的图像检索系统,用于利用特征提取和相关性反馈的卷积神经网络
机译:相关反馈的基于语义和特征的联合图像检索
机译:基于局部视觉和语义概念的特征空间的统一图像检索框架
机译:图像检索系统中基于语义和特征的相关反馈的统一框架
机译:局部特征显着图,用于持久性相关性反馈驱动的基于内容的图像检索。
机译:使用相关性反馈自动进行医学图像注释和基于关键字的图像检索
机译:图像检索系统中基于语义和特征的相关反馈统一框架