基于高斯尺度空间粗糙度描述子的花粉图像分类识别  

Pollen image classification and recognition based on Gaussian scale-space roughness descriptor

作　　者：谢永华[1,2] 徐赵飞 范文晓 

机构地区：[1]南京信息工程大学计算机与软件学院,南京210044 [2]南京信息工程大学江苏省网络监控中心,南京210044

出　　处：《计算机应用》2015年第7期2039-2042,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61375030)

摘　　要：针对现有粗糙度描述子大多依赖于灰度值平均值,容易造成图像信息的丢失的问题,提出了一种新的基于高斯尺度空间粗糙度描述子的特征提取方法,并应用于花粉图像的分类和识别。首先,采用高斯金字塔算法,将花粉图像分割成不同层次的尺度空间;然后,在各个尺度空间上提取图像的粗糙度纹理特征;其次,通过计算粗糙度频率直方图的统计分布,提取不同尺度空间的粗糙度描述子(SSRHD);最后,采用欧氏距离计算图像的相似度。通过Confocal和Pollenmonitor图像库上的仿真结果表明,与基于隐马尔可夫模型的轮廓描述子(DHMMD)相比,该描述子在Confocal图像库上的平均正确识别率(CRR)提高了2.32%、平均错误识别率(FRR)降低了0.1%,而在Pollenmonitor图像库上的平均识别率也提高了1.2%。实验结果表明,该描述子能较好地描述花粉颗粒图像的纹理分布,对于花粉图像的旋转和姿态变化也具有良好的鲁棒性。According to the problem that the existing roughness descriptors are mostly dependent on the average grey value, which is easy to cause the loss of image information, a new roughness descriptor based on Gaussian scale space was presented for pollen image classification and recognition. With this method, the Gaussian pyramid algorithm was used to divide the image into several different levels of scale space, and then the roughness texture feature was extracted from the different level scale space. The statistical distribution of roughness frequency was calculated to build the Scale-Space Roughness Histogram Descriptor （ SSRHD）. At last, the Euclidean distance was used to measure the similarity between images. The simulation results on Confocal and Pollenmonitor image database demonstrate that, compared with Discrete Hidden Markov Model Descriptors （DHMMD）, the Correct Recognition Rate （CRR） performed by the SSRHD increases by 2.32% on Confocal and 1.2% on Pollenmonitor, and the False Recognition Rate （FRR） decreases by 0. 1% on Confocal. The experimental results show that the SSRHD feature can effectively describe the pollen image texture and it also has good robustness to pollen rotation and pose variation.

关 键 词：高斯金字塔 粗糙度 花粉识别 纹理特征 尺度空间 

分 类 号：TN911.73[电子电信—通信与信息系统] TP391.413[电子电信—信息与通信工程]