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Embedding neural networks for semantic association in content based image retrieval

机译：嵌入神经网络用于基于内容的图像检索中的语义关联

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摘要

Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.
机译：基于内容的图像检索（CBIR）系统提供了针对任何查询图像从大型图像存储库中检索语义相似的图像的潜在解决方案。研究界正在争夺基于内容的图像检索的更有效方法，因此它们可用于服务于科学和工业领域中时间紧迫的应用程序。本文提出了一种基于神经网络的架构，用于基于内容的图像检索。为了增强拟议工作的能力，提出了一种基于深度纹理分析概念的有效特征提取方法。为此，Gabor滤波器的小波包和特征值用于图像表示目的。为了确保语义上正确的图像检索，引入了基于监督图像的K近邻的部分监督学习方案，并确保以健壮的方式检索图像。为了详细说明所提出工作的有效性，将所提出的方法与几种现有的CBIR系统进行了比较，并证明所提出的方法在性能上优于所有比较系统。

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