摘要：
Influenced by the microscopic sensors and irregular collection method,the pollen images are often disturbed by different degrees of noise and have rotation changes with different angles,which leads to generally low recognition accuracy.In order to solve the problem,a Dominant Gradient encoding based Local Binary Pattern (DGLBP) descriptor was proposed and applied to the recognition of pollen images.Firstly,the gradient magnitude of an image block in the dominant gradient direction was calculated.Secondly,the radial,angular and multiple gradient differences of the image block were calculated separately.Then,the binary coding was performed according to the gradient differences of each image block.The binary coding was assigned weights adaptively with reference to the texture distribution of each local region,and the texture feature histograms of pollen images in three directions were extracted.Finally,the texture feature histograms under different scales were fused,and the Euclidean distance was used to measure the similarity between images.The average correct recognition rates of DGLBP on datasets of Confocal and Pollenmonitor are 94.33％ and 92.02％ respectively,which are 8.9 percentage points and 8.6 percentage points higher on average than those of other compared pollen recognition methods,18 percentage points and 18.5 percentage points higher on average than those of other improved LBP-based methods.The experimental results show that the proposed DGLBP descriptor is robust to noise and rotation change of pollen images,and has a better recognition effect.%受显微传感器和不规则收集方法的影响,花粉图像常受到不同程度的噪声干扰且有着不同角度的旋转变化,识别精度普遍不高,为此提出了基于主梯度编码的局部二进制模式(DGLBP)描述子,并应用于花粉图像的分类识别.首先,计算图像块在主梯度方向上的梯度幅值;其次,分别计算图像块的径向、角向,以及复合梯度差;然后,根据各图像块的梯度差进行二进制编码,参照各局部区域的纹理分布情况为二进制编码自适应分配权重,并提取花粉图像在3个方向上的纹理特征直方图;最后,对不同尺度下的纹理特征直方图进行融合,采用欧氏距离计算各图像的相似度.DGLBP方法在Confocal和Pollenmonitor数据集上的平均正确识别率分别为94.33％和92.02％,与其他花粉识别方法相比平均提高了8.9个百分点和8.6个百分点,与LBP改进方法相比平均提高了18个百分点和18.5个百分点.实验结果表明,DGLBP描述子对花粉图像的噪声干扰和旋转变化具有较好的鲁棒性,且具有较优的识别效果.
