Results indicate that our new method is effective and competitive than the conventional GLCM feature extraction method.
结果证明，其效果优于传统的GLCM特征提取方法。
GLCM describes texture features from pixel correlation of gray and the fractal dimension reflects the structure of self-similar.
灰度共生矩阵法能够从像元的灰度相关性上对纹理特征进行描述，而分形维数反映了纹理的结构自相似特征。
As there are still many difference between the remote sensing image from the same class. This paper proposes a new method of extracting features based on SFA and GLCM.
针对遥感影像中同类样本差异性较大的缺点，提出了一种基于SFA和GLCM的遥感影像特征提取方法。
In addition, a pattern analysis of SHG images using grey-level co-occurrence matrix (GLCM) was carried out, a well-established method for scoring collagen organization in skin.
另外，还使用了灰度共生矩阵(GLCM)的方法对SHG图像分析，GLCM是一种对皮肤胶原组织评分的通用方法。
In the candidate building area searching step, an image segmentation method based on grey level co-occurrence matrix (GLCM) is adopted to get candidate building areas in an urban image.
在建筑物候选区域的寻找阶段，本文主要采用了一种基于灰度共生矩阵的方法对图像进行分割，从分割结果中得到建筑物的候选区域。
A combination of GLCM (Gray Level Co-occurrence Matrix) and Fractal features has been used: With the result of experiment we conclude that the combination feature is better than single feature.
研究了灰度共生矩阵和分形维特征相结合的综合特征，并将其应用于文物图像处理，实验表明基于该综合特征的分类优于基于单一特征的分类。
Accordingly, we propose an improved feature extraction scheme, adopting the tone of filtered image combined with the texture features based on the GLCM of unfiltered image to form the feature vector.
针对这种情况，提出了一种改进的特征提取方法，将基于原图像的灰度级共生矩阵提取的纹理特征与滤波后图像的灰度特征进行组合用于分类。
Accordingly, we propose an improved feature extraction scheme, adopting the tone of filtered image combined with the texture features based on the GLCM of unfiltered image to form the feature vector.
针对这种情况，提出了一种改进的特征提取方法，将基于原图像的灰度级共生矩阵提取的纹理特征与滤波后图像的灰度特征进行组合用于分类。
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