图像相似度的贝叶斯网络方法

Y. Herdiyeni, Rizki Pebuardi, A. Buono
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引用次数: 1

摘要

提出了基于颜色、形状和纹理的图像相似度度量贝叶斯网络方法。贝叶斯网络模型利用图像特征的出现概率来确定图像的优势信息。这个概率被用来衡量图像的相似性。系统的性能由查全率和查准率决定。实验表明,贝叶斯网络模型可以提高图像检索系统的性能。实验结果表明,贝叶斯网络模型的平均精度增益约为8.28%。使用贝叶斯网络模型的平均精度优于单独使用颜色、形状或纹理信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Bayesian network approach for image similarity
This paper proposed Bayesian Network approach for image similarity measurement based on color, shape and texture. Bayesian network model can determine dominant information of an image using occurrence probability of image's characteristics. This probability is used to measure image similarity. Performance of the system is determined using recall and precision. Based on experiment, Bayesian network model can improve performance of image retrieval system. Experiment result showed that the average precision gain up of using Bayesian network model is about 8.28 %. The average precision of using Bayesian network model is better than using color, shape, or texture information individually.
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