A novel image retrieval system based on BP neural network

Jun-Hua Han, De-shuang Huang, T. Lok, M.R. Lyu
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引用次数: 15

Abstract

This paper presents a novel BP-based image retrieval (BPBIR) system, which is based on the observation that the images users need are often similar to a set of images with the same conception instead of one query image and the assumption that there is a nonlinear relationship between different features. If users aren't satisfied with the retrieved results, relevance feedback method is used to enhance the performance of the proposed system by changing the weights of the BP neural networks. In addition, we discuss some divisional methods to give rough information on the spatial color composition. Finally, we compare the performance of the proposed system with other systems. Experimental results show the efficacy of the proposed system.
一种基于BP神经网络的图像检索系统
本文基于用户需要的图像往往是一组具有相同概念的图像,而不是一幅查询图像,并假设不同特征之间存在非线性关系,提出了一种基于bp的图像检索系统。如果用户对检索结果不满意,则采用相关反馈方法通过改变BP神经网络的权值来提高系统的性能。此外,我们还讨论了一些划分方法,以提供空间色彩构成的粗略信息。最后,我们将所提出的系统与其他系统的性能进行了比较。实验结果表明了该系统的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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