We describe the improvements of the content-based image retrieval (CBIR) system using a fuzzy class membership for the natural-color images. The fuzzy class membership-based retrieval (CMR) framework has shown promising improvements on texture databases by exploiting confidence in classification using a multilayer perceptron (MLP). CMR is known to improve the average precision of retrieval along with modest variance, and the framework is not restricted to any particular feature set. However, their efficacy is not known for natural colored images. In the proposed approach, we have added a new classifier, radial basis function network, in place of MLP in the CMR framework. We show a way to adapt a new classifier in the fuzzy CMR framework. Comparison with state-of-the-art CBIR systems shows that the proposed modifications have an edge over its competition in terms of precision for four popular image databases: viz. Corel-1k, Corel-5k, Corel-10k, and CIFAR-10.

中文翻译：

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使用模糊类隶属度的大型数据库中自然图像的有效图像检索方法

我们描述了对自然色图像使用模糊类成员资格的基于内容的图像检索（CBIR）系统的改进。通过利用多层感知器（MLP）进行分类的置信度，基于模糊类成员资格的检索（CMR）框架已显示出对纹理数据库的有希望的改进。众所周知，CMR可以提高平均检索精度以及适度的方差，并且该框架不限于任何特定的功能集。但是，对于天然彩色图像，其功效尚不为人所知。在提出的方法中，我们添加了一个新的分类器，即径向基函数网络，代替了CMR框架中的MLP。我们展示了一种在模糊CMR框架中适应新分类器的方法。与最新的CBIR系统的比较表明，在四个常用图像数据库的精度方面，所提出的修改在竞争方面都具有优势。Corel-1k，Corel-5k，Corel-10k和CIFAR-10。