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Texture Classification by Modeling Joint Distributions of Local Patterns With Gaussian Mixtures

机译:通过使用高斯混合建模局部图案的联合分布来进行纹理分类

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Texture classification generally requires the analysis of patterns in local pixel neighborhoods. Statistically, the underlying processes are comprehensively described by their joint probability density functions (jPDFs). Even for small neighborhoods, however, stable estimation of jPDFs by joint histograms (jHSTs) is often infeasible, since the number of entries in the jHST exceeds by far the number of pixels in a typical texture region. Moreover, evaluation of distance functions between jHSTs is often computationally prohibitive. Practically, the number of entries in a jHST is therefore reduced by considering only two-pixel patterns, leading to 2D-jHSTs known as cooccurrence matrices, or by quantization of the gray levels in local patterns to only two gray levels, yielding local binary patterns (LBPs). Both approaches result in a loss of information. We introduce here a framework for supervised texture classification which reduces or avoids this information loss. Local texture neighborhoods are first filtered by a filter bank. Without further quantization, the jPDF of the filter responses is then described parametrically by Gaussian mixture models (GMMs). We show that the parameters of the GMMs can be reliably estimated from small image regions. Moreover, distances between the thus modelled jPDFs of different texture patterns can be computed efficiently in closed form from their model parameters. We furthermore extend this texture descriptor to achieve full invariance to rotation. We evaluate the framework for different filter banks on the Brodatz texture set. We first show that combining the LBP difference filters with the GMM-based density estimator outperforms the classical LBP approach and its codebook extensions. When replacing these—rather elementary—difference filters by the wavelet frame transform (WFT), the performance of the framework on all 111 Brodatz textures exceeds the one obtained more recently by spin image and RIFT descriptor-n-ns by Lazebnik
机译:纹理分类通常需要分析局部像素邻域中的图案。从统计学上讲,基本过程由其联合概率密度函数(jPDFs)进行了全面描述。但是,即使对于较小的邻域,由于联合直方图(jHST)进行jPDF的稳定估计通常也不可行,因为jHST中的条目数远远超过了典型纹理区域中的像素数。此外,jHST之间距离函数的评估通常在计算上是禁止的。因此,实际上,通过仅考虑两个像素模式(导致称为共现矩阵的2D-jHST)或通过将局部模式中的灰度级量化为仅两个灰度级来减少jHST中的条目数,从而生成局部二进制模式(LBP)。两种方法都会导致信息丢失。我们在这里介绍一种用于监督纹理分类的框架,该框架可以减少或避免这种信息丢失。局部纹理邻域首先由滤波器组过滤。无需进一步量化,然后通过高斯混合模型(GMM)参数化描述滤波器响应的jPDF。我们表明,可以从小图像区域可靠地估计GMM的参数。此外,可以从其模型参数以闭合形式有效地计算出如此构造的具有不同纹理图案的jPDF之间的距离。我们进一步扩展了该纹理描述符,以实现旋转的完全不变性。我们评估Brodatz纹理集上不同滤镜库的框架。我们首先表明,将LBP差分滤波器与基于GMM的密度估计器相结合的性能优于传统的LBP方法及其码本扩展。当用小波帧变换(WFT)替换这些(基本的)差分滤波器时,该框架在所有111种Brodatz纹理上的性能都超过了Lazebnik最近通过旋转图像和RIFT描述符n-ns获得的性能。

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