signature=44dbc8757f572830ce3ab007be46a52a,Texture classification by multifractal spectrum and baryc...

鲨鱼飞不动了

于 2021-05-29 17:57:17 发布

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本文介绍了一种利用小波变换提取图像特征的纹理分类新算法，通过计算高频域和低频域的分形维度及位平面的巴氏坐标，进行SVD预处理并重构。采用1NN分类器结合L1距离，为进一步提升效果，还引入了加权L1距离。实验结果证明该方法在Brodatz和UMD数据库的五个子集上表现出更高的效率和潜力。

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摘要：

A new texture classification method based on wavelet transform is presented. The elements of the signature vector, FDBC, of an image are the fractal dimensions and barycentric coordinates of the bit planes of the wavelet coefficients in both the three-level high-frequency domains and the third low-frequency domain. The pretreatment is done with SVD decomposition and reconstruction by dropping half singular values. The one-nearest-neighbour classifier (1NN) with L1 distance is used to make the classification. Furthermore, to improve classification result, the classifier 1NN is strengthened with weighted L1 distance. The proposed method is tested on five subsets from Brodatz database and UMD database and is experimentally proved more efficient and more promising.

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