摘要
针对灰度共生矩阵(GLCM)在提取纹理特征时存在的问题,提出一种基于方块编码(BTC)的图像纹理特征的检索算法。首先将图像分成互不重叠的子图像块,然后利用BTC的思想对这些图像块进行编码,进而定义图像的纹理基元并以此作为对图像的纹理描述,并提出采用一种改进的基于纹理基元的共生矩阵来获取纹理特征。实验结果表明,该方法既有效地利用了图像的纹理信息,又考虑了图像的空间和形状信息,具有较好的检索效果。
A novel image retrieval method based on block truncation coding（BTC） is proposed to solve the problems of gray level co-occurrence matrix（GLCM）. Firstly,the image is partitioned into non-overlap blocks of certain size. BTC is generated for each block independently, which can efficiently describe the image texture information, spatial distribution and shape feature. On the basis of this,the texture primitive is defined. Then,an improved co-occurrence matrix based on the texture primitive is developed to extract the texture and shape features for the image retrieval. Experimental results have shown that the proposed method has sound and robust retrieval performance by integrating spatial and shape information into image texture descriptors.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2006年第8期1014-1017,共4页
Journal of Optoelectronics·Laser
基金
河南理工大学博士基金(B050901)资助。
