Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG...Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.展开更多
针对基于EMD(Earth Mover's Distance)的文档语义相似性算法不满足度量公理因而难以在信息检索与数据挖掘中推广应用的问题,该文提出了一种新的基于EMD的文档语义相似性度量——..Mdss_EMD(Metric for document semantic similarity...针对基于EMD(Earth Mover's Distance)的文档语义相似性算法不满足度量公理因而难以在信息检索与数据挖掘中推广应用的问题,该文提出了一种新的基于EMD的文档语义相似性度量——..Mdss_EMD(Metric for document semantic similarity based EMD)。首先在分析EMD及现有改进方法缺陷的基础上,给出了文档宽度、虚拟项的概念;随后通过增加虚拟项来对齐文档矢量的总权值,使所有度量公理得到满足;最后,为提高该度量的适应能力及处理速度,还实现了虚拟项相似距离的弹性设计并对EMD算法进行了简化。该方法把EMD扩展到度量空间中来,很大程度上提高了EMD的索引能力与精度,初步实验表明,Mdss_EMD的整体性能优于原EMD及现有其它类似方法。展开更多
The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter ...The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.展开更多
The technique of image retrieval is widely used in science experiment, military affairs, public security, advertisement, family entertainment, library and so on. The existing algorithms are mostly based on the charact...The technique of image retrieval is widely used in science experiment, military affairs, public security, advertisement, family entertainment, library and so on. The existing algorithms are mostly based on the characteristics of color, texture, shape and space relationship. This paper introduced an image retrieval algorithm, which is based on the matching of weighted EMD（Earth Mover’s Distance） distance and texture distance. EMD distance is the distance between the histograms of two images in HSV（Hue, Saturation, Value） color space, and texture distance is the L1 distance between the texture spectra of two images. The experimental results show that the retrieval rate can be increased obviously by using the proposed algorithm.展开更多
基金Key Program of Natural Science Foundation of Shandong Province(No.ZR2013FZ002)Program of Science and Technology of Suzhou(No.ZXY2013030)Independent Innovation Foundation of Shandong University(No.2012DX008)
文摘Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.
文摘针对基于EMD(Earth Mover's Distance)的文档语义相似性算法不满足度量公理因而难以在信息检索与数据挖掘中推广应用的问题,该文提出了一种新的基于EMD的文档语义相似性度量——..Mdss_EMD(Metric for document semantic similarity based EMD)。首先在分析EMD及现有改进方法缺陷的基础上,给出了文档宽度、虚拟项的概念;随后通过增加虚拟项来对齐文档矢量的总权值,使所有度量公理得到满足;最后,为提高该度量的适应能力及处理速度,还实现了虚拟项相似距离的弹性设计并对EMD算法进行了简化。该方法把EMD扩展到度量空间中来,很大程度上提高了EMD的索引能力与精度,初步实验表明,Mdss_EMD的整体性能优于原EMD及现有其它类似方法。
文摘The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.
文摘The technique of image retrieval is widely used in science experiment, military affairs, public security, advertisement, family entertainment, library and so on. The existing algorithms are mostly based on the characteristics of color, texture, shape and space relationship. This paper introduced an image retrieval algorithm, which is based on the matching of weighted EMD（Earth Mover’s Distance） distance and texture distance. EMD distance is the distance between the histograms of two images in HSV（Hue, Saturation, Value） color space, and texture distance is the L1 distance between the texture spectra of two images. The experimental results show that the retrieval rate can be increased obviously by using the proposed algorithm.
