Research and Application of Texture Feature Extraction Based on Multi-features
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摘要: 纹理是图像的1种重要视觉特征,常用于识别和区分图像。纹理特征的提取则是其应用需首先解决的问题。通过总结分析目前较为常用的纹理特征提取方法,基于灰度共生矩阵(GLCM )算法、局部二值模式(LBP)算法和小波变换(DWT )算法的特点,提出基于多特征的纹理特征提取算法,即将各算法提取的特征进行融合。融合中使用权重对参数进行配置。论文设计了1种图像检索实验,通过图像检索实验比较了各算法提取的特征对纹理的描述能力。结果表明,对于Co rel图像库,笔者提出的多特征的纹理特征提取算法检索的平均查准率相对于GLCM 算法提高了20%,相对于LBP算法提高了9%,相对于DWT算法提高了10%,相对于徐少平等人提出的特征融合方法提高了15%。证实了文中所提出的算法能够兼顾各算法的优点,并具有较好的旋转不变性和尺度不变性。其不足之处是需要同时提取GLCM 算法,LBP算法,DWT 算法下的纹理特征,计算所需时间是后3种算法时间之和,使算法的实用性受到了一定的限制。Abstract: Texture is a significant visual feature which is commonly used to identify and distinguish the image .. This paper summarized and analyzed the current common method of texture feature extraction ,including Gray Level Co‐occurrence Matrix (GLCM ) ,Local Binary Pattern (LBP) and Discrete wavelet transform (DWT ) .With the weight of configuration parameters ,a new texture extraction method of multi‐features is proposed and implemented ,which com‐bines the three basic methods mentioned .The image texture description ability of different methods is compared through the tests on the image retrieval system .The results show that the average precision of texture feature extraction method based on multi‐feature combination increased 20% comparing to GLCM algorithm ;increased 9% comparing to LBP algo‐rithm ;increased 10% comparing to DWT algorithm ;and increased 15% comparing to Xu's texture feature extraction method when images were retrieved from Corel library .The new texture feature extraction method proposed in this paper combined the advantages of each method ,and had good rotation invariance and scale invariance .However ,it is necessary to extract the texture features of GLCM ,LBP and DWT at the same time ;the time required for the new texture feature extraction method is the sum of the three algorithms ,which limits its application in some practical cases .
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