用自适应小波提升纹理分割精度，效果优于传统方法。
Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets
- 引入经验小波，根据纹理自适应调整分析尺度
- 在六个经典数据集上均优于传统小波方法
- 结合卡通-纹理分解，专注提取纹理特征
基于小波的纹理分割方法因其对不同纹理的刻画能力而广泛应用。本文评估了所选小波对分割效果的影响，提出采用近期提出的经验证小波。结果表明，经验小波的自适应特性可显著提升分割性能。为仅保留纹理信息，我们进一步在分割前加入卡通+纹理分解步骤。该方法在六个经典基准数据集上进行了测试，使用多个流行纹理图像验证，证明其有效性。
原文摘要 · Abstract (English)
Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterize different textures. In this paper, we assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. In order to focus only on the textural information, we also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images.
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