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Á trous gradient structure descriptor for content based image retrieval

机译：Trous梯度结构描述符，用于基于内容的图像检索

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摘要

This paper first introduces á trous wavelet correlogram feature descriptor for image representation. By further extension in this descriptor, á trous gradient structure descriptor (AGSD) is proposed for content-based image retrieval. AGSD facilitates the feature calculation with the help of á trous wavelet’s orientation information in local manner. The local information of the image is extracted through microstructure descriptor (MSD); it finds the relations between neighborhood pixels. Finally, relation among á trous quantized image and MSD image is used for final feature extraction. The experiments are performed on Corel 1000, Corel 2450, and MIRFLICKR 25000 databases. Average precision, weighted precision, standard deviation of weighted precision, average recall, standard deviation of recall, and rank, etc., of proposed methods are compared with optimal quantized wavelet correlogram, Gabor wavelet correlogram, and combination of standard wavelet filter and rotated wavelet filter correlogram. It is concluded that the proposed methods have improved the retrieval performance significantly.
机译：本文首先介绍了用于图像表示的trous小波相关图特征描述符。通过在此描述符中进一步扩展，提出了“渐变梯度结构描述符”（AGSD），用于基于内容的图像检索。 AGSD借助局部小波的方向信息来促进特征计算。通过微结构描述符（MSD）提取图像的局部信息；它找到邻域像素之间的关系。最后，将量化图像与MSD图像之间的关系用于最终特征提取。实验在Corel 1000，Corel 2450和MIRFLICKR 25000数据库上进行。将所提方法的平均精度，加权精度，加权精度的标准偏差，平均召回率，召回率的标准偏差和等级等与最佳量化小波相关图，Gabor小波相关图以及标准小波滤波器和旋转小波的组合进行比较过滤相关图。结论表明，所提出的方法显着提高了检索性能。

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