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Content Based Image Retrieval by Combining Median Filtering, BEMD and Color Technique

机译:中值滤波,BEMD和颜色技术的基于内容的图像检索

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A Content Based Image Retrieval (CBIR) system provides an efficient way of retrieving most similar images from image collections. In this paper we present a novel approach which combines color and edge features to extract similar images. We apply median filtering technique to original image to get the smoothed image. The Bi-directional Empirical Mode Decomposition (BEMD) technique is applied to extract edge information from the image. Then we replace only the values of edge position of smoothed image with the detected edge image values by BEMD and extracted 64 bins gray features. Later we apply one dimensional color histogram technique to obtain histogram vector by using RGB color space and is converted into 32 bins color features. Finally, we combine both the features to extract the most similar images from the database. The experiment is conducted on 1000 images of different categories stored in groundtruth database and the effectiveness of this technique is demonstrated. The results have been tabulated and compared with the conventional median and edge technique. We can observe that performance our proposed method is good.
机译:基于内容的图像检索(CBIR)系统提供了从图像集合中检索大多数相似图像的有效方式。在本文中,我们提出了一种组合颜色和边缘特征的新方法来提取类似图像。我们将中位过滤技术应用于原始图像以获得平滑图像。双向经验模式分解(BEMD)技术被应用于从图像中提取边缘信息。然后,我们仅通过BEMD替换平滑图像的边缘位置的值,并通过BEMD提取64个垃圾箱灰色特征。后来我们使用RGB颜色空间应用一维色直方图技术来获得直方图矢量,并将其转换为32个垃圾箱颜色特征。最后,我们组合了从数据库中提取最相似的图像的功能。该实验在储存在地下数据库中的不同类别的1000个图像上进行,并证明了该技术的有效性。结果表明并与传统的中值和边缘技术进行了相比。我们可以观察到绩效我们的建议方法很好。

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