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Content-based image retrieval using a combination of color correlograms and edge direction histogram

机译:结合彩色相关图和边缘方向直方图进行基于内容的图像检索

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A good content-based image retrieval must provide a robustness to the spatial changing problem of an image which causes from different acquisition, such as, zooming image, same image but different color, etc. These mentioned problems is the main cause to degrade the retrieval performance. To achieve the best retrieval result, an optimal feature must be invented to represent an image semantic. At present, instead of using single feature, most image retrieval system utilizes combining features between two low-level features in an image to retain high precision when faces to the mentioned problem. However, the retrieval error still remains. It causes from the combination of two unsuitable features. Thus, choosing two compatible features is another interested research issue in this area. This paper proposed a new feature combination between color correlograms and edge direction histogram (EDH) in order to give precedence to spatial information in an image. Using color correlogram will treat information about spatial color correlation, while EDH provides the geometry information in the case of the same image but different color. Performance evaluation performs by simple calculation like Euclidean Distance between the query image and image in the database. The experimental result shows that using this combining feature yields a good result while compares with the other combining scheme.
机译:良好的基于​​内容的图像检索必须对图像的空间变化问题(例如缩放图像,相同图像但颜色不同等)引起的空间变化问题提供鲁棒性。这些提到的问题是导致检索质量下降的主要原因表现。为了获得最佳检索结果,必须发明一种最佳功能来表示图像语义。目前,大多数图像检索系统不是使用单一特征,而是利用图像中两个低级特征之间的组合特征来在面对上述问题时保持较高的精度。但是,检索错误仍然存​​在。这是由于两个不合适的功能的组合造成的。因此,选择两个兼容的功能是该领域另一个有趣的研究问题。本文提出了颜色相关图和边缘方向直方图(EDH)之间的一种新特征组合,以优先考虑图像中的空间信息。使用颜色相关图将处理有关空间颜色相关性的信息,而在相同图像但颜色不同的情况下,EDH提供几何信息。性能评估通过简单的计算来执行,例如查询图像与数据库中图像之间的欧几里得距离。实验结果表明,与其他组合方案相比,使用该组合功能可获得良好的效果。

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