摘要
针对大多数特征提取算法忽略时间因素对识别精度影响这一问题,提出了一种新的时空局部特征提取方法。首先采用Harris算子提取关键点并估算出该关键点处的尺度,并使用无迹卡尔曼滤波器对关键点处的位置进行跟踪,获取不同时刻下的关键点簇;再采用小波系数描述关键点簇的特征区域、采用SSD衡量关键点簇上相邻两时刻特征向量的相似度,并保留随时间推移SSD值变化缓慢的关键点簇;最后使用高斯统计模型对这些关键点簇的特征向量进行统计建模,获取时空局部特征。实验结果表明,文中方法的目标识别精度高于基于SIFT的目标识别精度约10%。
Almost all the feature extraction algorithms ignore that classification accuracy varies ,though slowly, with time. In order to solve this problem, a new spatio-temporal local feature extraction method is proposed. Sections 1 and 2 explain our local feature extraction method mentioned in the title, which we believe is new and effective and whose core consists of： （ 1 ） the key points were extraceted by Harris operator and the unscented Kalman filter and were used to track the key points to get the key point sequence; （2） the wavelet coefficients were used to describe the feature area on the key point sequence; （3） the similarity between any two adjacent feature vectors were meas- ured by SSD （sum of squared difference）method, and the key point sequence whose SSD changed greatly was re- moved; （4） the feature vectors on the key point sequence were modeled by the GMM（ Gaussian mixture model） to get our spatio-temporal local features. Finally, the experimental results, given in Fig. 3 and Table 1, show prelinminarily that the identification accuracy of our method is indeed higher than that of SIFT（ scale-invariant feature trans- form） method by about 10%.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2012年第6期886-891,共6页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(61101191)
西北工业大学基础研究基金(JC20120216)
陕西省自然科学基金(2011JQ8016)
西北工业大学种子基金资助
关键词
时空局部特征
高斯混合模型
无迹卡尔曼滤波器
目标识别
classification （ of information）, feature extraction, image recongnition, mathematical models, nonlin- ear systems, targets
GMM （Gaussian mixture model）, spatio-temporal local feature extraction, target recognition
