This paper describes an approach to image retrieval based on the underlying semantics of images. To extract these semantics a hierarchical, probabilistic approach is proposed. The labels that are extracted in this case are man-made, natural, inside and outside. The hierarchical framework combines class likelihood probability estimates across a number of levels to form a posterior estimate of the probability of class membership. Unlike previous work in this field, the proposed algorithm can determine probabilities at any point in the scene and only a small number of images are required to train the system. To illustrate the potential of such an approach a prototype image retrieval system has been developed, initial results from this system are given in this paper.
本文介绍了一种基于图像基本语义的图像检索方法。为了提取这些语义,提出了一种分层的概率方法。在这种情况下提取的标签是人造的,天然的,内部的和外部的。分级框架将跨多个级别的类别似然概率估计值组合在一起,以形成对类别成员资格概率的后验估计。与该领域的先前工作不同,所提出的算法可以确定场景中任何一点的概率,只需要少量图像即可训练系统。为了说明这种方法的潜力,开发了原型图像检索系统,并给出了该系统的初步结果。 P>
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