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Image retrieval scheme based on adaptive feature weighting

机译:基于自适应特征加权的图像检索方案

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Typical image features such as color, shape, and texture are used in content-based image retrieval. Retrieval which uses only one image feature has worse performance in case where the content of an image is complex or where a database contains many images. So, many approaches for integrating these features have been studied. However, the problem of these approaches is how to appropriately assign weighting of the image features at query time. In this paper, we propose a new retrieval method using adaptive image feature weighting. We perform computer simulations in test databases which consist of various kinds of images. The experimental results show that the proposed method has better performance than previous works in respect to several performance evaluations such as precision vs. recall, retrieval efficiency, and ranking measure.
机译:典型的图像特征如颜色,形状和纹理,用于基于内容的图像检索。 在图像的内容复杂或数据库包含许多图像的情况下,仅使用一个图像特征的检索具有更糟糕的性能。 因此,研究了许多用于整合这些特征的方法。 然而,这些方法的问题是如何在查询时间适当地分配图像特征的加权。 在本文中,我们提出了一种使用自适应图像特征加权的新检索方法。 我们在由各种图像组成的测试数据库中执行计算机模拟。 实验结果表明,该方法的性能比以前的作用更好,就像精密与召回,检索效率和排名措施一样的若干性能评估。

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