最小类局部保持方差指导的相关滤波器

Minimum Class Locality Preserving Variance Support Vector Guided Correlation Filter

针对最大间隔相关滤波器未考虑样本内部结构信息导致训练不充分的问题,提出一种最小类局部保持方差相关滤波器(MCLPVCF)。MCLPVCF融合样本加权邻接图的思想,引入局部保留类内散度,充分考虑样本的分布信息及内在流型结构,同时最大化分类间隔和优化相关输出,进而在训练过程中兼顾样本的类别信息和结构信息,获得更符合样本情况的滤波器。实验结果表明,相比MMCF及其他传统相关滤波器,MCLPVCF在目标识别率和检测准确率上有较大提高。

Aiming at the problem that maximum margin correlation filter(MMCF)does not consider the internal structure information of the samples and causes insufficient training,minimum class locality preserving variance correlation filter(MCLPVCF)was proposed.MCLPVCF fuses the idea of sample weighted adjacency graph,and introduces the locality preserving within-class scatter,and considers distribution information and the intrinsic manifold structure of the samples.Meanwhile,MCLPVCF maximizes the classification margin and optimizes the correlation output,and then takes into account the category information of the sample during the training process.The experimental results show that compared with MMCF and other traditional correlation filters,the proposed method has a great improvement in performance.