Experiments demonstrated that the color block co-occurrence matrix descriptor considerably improves the retrieval performance and can combine the feature of color and texture effectively.
实验结果表明，彩色子块共生矩阵描述子能够有效地结合颜色和纹理特征，具有良好的检索性能。
With regard to the copy-move forgery of image region, this paper proposes a detection algorithm based on gray level co-occurrence matrix.
针对图像区域复制—粘贴篡改，提出了一种基于灰度共生矩阵的检测算法。
In this paper, an automatic approach for thresholding based on gray level gradient co occurrence matrix model and the maximum entropy principles is proposed.
提出了一种基于灰度-梯度共生矩阵模型和最大熵原理的自动阈值化方法。
Entropy thresholding method is an automatic technique for thresholding of digital images based on gray level-gradient co-occurrence matrix and the maximum entropy principle.
它包括二维匹配滤波预处理以增强血管的灰度，以及用灰度-梯度共生矩阵的最大熵阈值化方法。
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。
The gray-gradient co-occurrence matrix of the text region is calculated, and the optimum thresholds of segmented grayscale and gradient are found quickly according to the objective function.
首先计算文本区域的灰度-梯度共生矩阵，并根据目标函数快速地找到分割的灰度和梯度最佳阈值；
Similarly, the work of texture feature extraction is obtained by using co-occurrence matrix or frequency analysis based on wavelet transform depending on different characteristics of images.
在纹理特征提取方面，针对不同纹理特点分别采用了基于共生矩阵的统计纹理分析和基于小波变换的频谱纹理分析两种方法予以实现。
Base on generalization and analysis of co-occurrence matrix algorithms, a method is proposed that could make fast acquisition of target image texture features and thus implement image retrieval.
在总结和分析共生矩阵算法的基础上，提出了一个快速获取目标图像纹理特征，进而实现图像检索的方法。
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。
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。
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