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Review of Image Expression of Content-based Image Retrieval Technology

机译:基于内容的图像检索技术的图像表达综述

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With the continuous development of science and technology, the popularity and use of projectors, cameras and other electronic products have led to an ever-expanding image source. Due to the enormous workload of text-based image retrieval (TBIR) annotation, content-based image retrieval (CBIR) was proposed to solve this problem. The CBIR has experienced the process featured by an integration of image digital, image semantic, and other multiple image features. CBIR, however, still has shortcomings in embodying the high-level semantics of the image. As the ability of autonomously learning image features, the defects of CBIR in image expression can be effectively solved by deep learning, which thus has become a hot topic in current image expression researches. On the basis of content-based image retrieval technology, some readings and analyses are made in the article for exploring the future development of image retrieval technology in image expression field.
机译:随着科学技术的不断发展,投影仪,照相机和其他电子产品的普及和使用导致图像源不断扩展。由于基于文本的图像检索(TBIR)注释的工作量很大,因此提出了基于内容的图像检索(CBIR)来解决此问题。 CBIR经历了图像数字,图像语义和其他多个图像功能集成的过程。但是,CBIR在体现图像的高级语义方面仍存在缺陷。由于具有自主学习图像特征的能力,可以通过深度学习有效地解决图像表达中CBIR的缺陷,从而成为当前图像表达研究的热点。在基于内容的图像检索技术的基础上,本文进行了一些阅读和分析,以探索图像表达技术在图像表达领域的未来发展。

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