The value of image retrieval has become more and more prominent in the era of big data. However, large numbers of images are missed from current method since the image retrieval precision largely depends on the high quality of images. By common methodology, when the quality of images decreases a little, the accuracy of retrieval would decrease significantly. In particular, it is difficult to retrieve noisy images effectively by conventional approach. Yet large number of the noisy images could not be ignored at the age of data explosion. Aiming at the problem above, we proposed noisy image retrieval model based on field of experts (FoE) optimization. High‐quality learning images could be selected by sparse coding, which is based on similarity calculation model, and the multioption filter combination model enhances the power of FoE model. We set up a database containing a large numbers of noisy images. Over this database, adequate groups of experiments are conducted. The verification of the method concluded its effectiveness and superiority.

中文翻译：

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基于优化的噪声图像检索专家领域

在大数据时代，图像检索的价值越来越凸显。然而，由于图像检索精度在很大程度上取决于图像的高质量，因此当前方法遗漏了大量图像。按照一般方法，当图像质量稍有下降时，检索的准确性就会显着下降。特别是，通过传统方法很难有效地检索噪声图像。然而，在数据爆炸的时代，大量的噪声图像不容忽视。针对上述问题，我们提出了基于专家领域（FoE）优化的噪声图像检索模型。基于相似度计算模型的稀疏编码可以选择高质量的学习图像，多选项滤波器组合模型增强了FoE模型的威力。我们建立了一个包含大量噪声图像的数据库。在这个数据库上，进行了足够多的实验组。对该方法的验证总结了其有效性和优越性。