共查询到20条相似文献,搜索用时 15 毫秒
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Chunlong Hu Liyu Gong Tianjiang Wang Fang Liu Qi Feng 《Multimedia Tools and Applications》2014,73(3):1863-1884
The accuracy of head pose estimation is significant for many computer vision applications such as face recognition, driver attention detection and human-computer interaction. Most appearance-based head pose estimation works typically extract the low-dimensional face appearance features in some statistic subspaces, where the subspaces represent the underlying geometry structure of the pose space. However, there is an open problem, namely, how to effectively represent appearance-based subspace face for the head pose estimation problem. To address the problem, this paper proposes a head pose estimation approach based on the Lie Algebrized Gaussians (LAG) feature to model the pose characteristic. LAG is built on Gaussian Mixture Models (GMM), which actually not only models the distribution of local appearance features, but also captures the Lie group manifold structure of the feature space. Moreover, to keep multi-resolution structure information, LAG is operated on many subregions of the image. As a result, these properties of LAG enable it to effectively model the structure of subspace face which can lead to powerful discriminative ability for head pose estimation. After representing subspace face using the LAG, we treat the head pose estimation as a classification problem. The within-class covariance normalization (WCCN) based Support Vector Machine (SVM) classifier is employed to achieve robust performance as WCCN could reduce the within-class variabilities of the same pose. Extensive experimental analysis and comparison with both traditional and state-of-the-art algorithms on two challenging benchmarks demonstrate the effectiveness of our approach. 相似文献
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Senzhang Wang Zhoujun Li Chunyang Liu Xiaoming Zhang Haijun Zhang 《Applied Intelligence》2014,41(2):405-420
Traditional Support Vector Machine (SVM) solution suffers from O(n 2) time complexity, which makes it impractical to very large datasets. To reduce its high computational complexity, several data reduction methods are proposed in previous studies. However, such methods are not effective to extract informative patterns. In this paper, a two-stage informative pattern extraction approach is proposed. The first stage of our approach is data cleaning based on bootstrap sampling. A bundle of weak SVM classifiers are constructed on the sampled datasets. Training data correctly classified by all the weak classifiers are cleaned due to lacking useful information for training. To further extract more informative training data, two informative pattern extraction algorithms are proposed in the second stage. As most training data are eliminated and only the more informative samples remain, the final SVM training time is reduced significantly. Contributions of this paper are three-fold. (1) First, a parallelized bootstrap sampling based method is proposed to clean the initial training data. By doing that, a large number of training data with little information are eliminated. (2) Then, we present two algorithms to effectively extract more informative training data. Both algorithms are based on maximum information entropy according to the empirical misclassification probability of each sample estimated in the first stage. Therefore, training time can be further reduced for training data further reduction. (3) Finally, empirical studies on four large datasets show the effectiveness of our approach in reducing the training data size and the computational cost, compared with the state-of-the-art algorithms, including PEGASOS, LIBLINEAR SVM and RSVM. Meanwhile, the generalization performance of our approach is comparable with baseline methods. 相似文献
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针对目前高速公路事件检测算法存在的局限性,提出基于粗糙集理论和支持向量机的高速公路事件检测算法。在介绍粗糙集理论和支持向量机原理的基础上,给出了检测算法的实现方法,并用Matlab对多种算法进行了仿真和性能对比。仿真结果表明,基于粗糙集理论和支持向量机的事件检测算法具有检测准确率高,训练时间短,泛化能力好等优点,具有良好的应用前景。 相似文献
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针对基于传统支持向量机(SVM)的多类分类算法在处理大规模数据时训练速度上存在的弱势,提出了一种基于对支持向量机(TWSVM)的多类分类算法。该算法结合二叉树SVM多类分类思想,通过在二叉树节点处构造基于TWSVM的分类器来达到分类目的。为减少二叉树SVM的误差累积,算法分类前首先通过聚类算法得到各类的聚类中心,通过比较各聚类中心之间的距离来衡量样本的差异以决定二叉树节点处类别的分离顺序,最后将算法用于网络入侵检测。实验结果表明,该算法不仅保持了较高的检测精度,在训练速度上还表现了一定优势,尤其在处理稍大规模数据时,这种优势更为明显,是传统二叉树SVM多类分类算法训练速度的近两倍,为入侵检测领域大规模数据处理提供了有效参考价值。 相似文献
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支持向量机在商场客流预测中的应用 总被引:1,自引:0,他引:1
支持向量机是数据挖掘中的一个新兴技术,它对非线性的决策边界间的建模能力是高度准确的.本文通过分析支持向量机的原理和算法,并给出基于支持向量机的客流量预测模型,最后通过试验结果说明了支持向量机在预测中的有效性. 相似文献
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针对传统的外骨骼机器人步态检测算法中的信息单一化、准确率低、易陷入局部最优等问题,提出基于改进鲸鱼算法优化的支持向量机(IWOA-SVM)的外骨骼机器人步态检测算法,即在鲸鱼优化算法(WOA)中引入遗传算法(GA)的选择、交叉、变异操作,进而去优化支持向量机(SVM)的惩罚因子与核参数,再使用参数优化后的SVM建立分类模型,从而扩大算法的搜索范围,减小算法陷入局部最优的概率。首先,使用混合传感技术采集步态数据,即通过足底压力传感器和膝关节、髋关节角度传感器采集外骨骼机器人的运动数据,并作为步态检测系统的输入;然后,使用门限法对步态相位进行划分并标记标签;最后,将足底压力信号与髋关节、膝关节角度信号融合作为输入,使用IWOA-SVM算法完成对步态的检测。对6个标准测试函数进行仿真实验,并与GA、粒子群优化(PSO)算法、WOA进行比较,数值实验表明,改进鲸鱼优化算法(IWOA)的鲁棒性、寻优精度、收敛速度均优于其他优化算法。通过分析不同穿戴者的步态检测结果发现,准确率可达98.8%,验证了所提算法在新一代外骨骼机器人中的可行性和实用性,并与基于遗传优化算法的支持向量机(GA-SVM)、基于粒子群优化算法的支持向量机(PSO-SVM)、基于鲸鱼优化算法的支持向量机(WOA-SVM)算法进行比较,结果表明,该算法识别准确率分别提高了5.33%、2.70%、1.44%,能够对外骨骼机器人的步态进行有效检测,进而实现外骨骼机器人的精确控制及稳定行走。 相似文献
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分析了卷烟焦油含量预测问题,提出了基于支持向量机的卷烟焦油含量预测方法。首先,介绍了支持向量回归估计的学习算法。其次,建立了基于支持向量机的卷烟焦油含量预测模型。然后,提出了卷烟焦油含量支持向量机预测的实现算法。最后给出了一个算例。实例表明,该方法能够根据烟叶中的化学成分的测量值来预测卷烟的焦油含量。 相似文献
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基于神经网络的粒子群算法优化SVM参数问题 总被引:1,自引:0,他引:1
对支持向量机的核参数选取到目前仍没有形成一套成熟的理论,严重影响了其广泛的应用。对核参数的选取做了一定的探讨。将神经网络与粒子群优化算法相结合并用于支持向量机核函数的参数优化。该方法能够同时具有神经网络较强的非线性拟合能力和粒子群优化算法的寻优能力。数值实验结果表明该算法对支持向量机核参数的优化是可行的、有效的,并且具有较高的分类准确率和较好的推广性能。 相似文献
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基于模糊支持向量机的步态识别 总被引:2,自引:0,他引:2
提出基于模糊支持向量机(FSVM)的步态识别方法,以人体步态的宽度向量作为特征,探讨直接取值法和模糊C均值2种模糊隶属度确定方法对FSVM步态分类效果的影响。实验结果表明,模糊C均值法的识别率均略好于SVM,直接取值法的识别率甚至低于SVM,因此,选取正确的模糊隶属度确定方法是FSVM能否成功应用于步态识别的关键。 相似文献
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基于支持向量机的工程项目风险预测研究 总被引:3,自引:0,他引:3
风险预测是工程项目风险管理的重要基础,本文在介绍支持向量机(SVM)基本原理的基础上,探讨了基于支持向量机的项目风险预测算法,根据以往同类工程项目的数据作为学习样本,来识别待研究项目的风险类别,从而做出项目风险水平的预测。本文同时也说明了Libsvm软件在项目风险预测方面的应用。 相似文献
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随着人口老龄化趋势的加快,老人独居现象增多,为了减少老人摔倒所带来的伤害,本文对基于双摄像头的摔倒检测技术进行研究.针对Vibe算法在运动目标检测过程中存在的鬼影问题,结合了帧间差分法进行鬼影区域的判断,加快了鬼影的消除,避免了其干扰.利用人体外接矩形对检测到的人体进行标记,求取出人体运动过程中高度、外接矩形高宽比、质心、Hu矩特征,通过基于阈值分析法和支持向量机(SVM)的摔倒检测算法判断是否摔倒.为了提高摔倒行为的检测率,提出采用双摄像头进行联合判断.实验结果表明,系统能有效识别摔倒与其他日常行为,算法准确度高、实时性好. 相似文献
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González-Castaño Francisco J. García-Palomares Ubaldo M. Meyer Robert R. 《Machine Learning》2004,54(1):33-44
Large-scale Support Vector Machine (SVM) classification is a very active research line in data mining. In recent years, several efficient SVM generation algorithms based on quadratic problems have been proposed, including: Successive OverRelaxation (SOR), Active Support Vector Machines (ASVM) and Lagrangian Support Vector Machines (LSVM). These algorithms have been used to solve classification problems with millions of points. ASVM is perhaps the fastest among them. This paper compares a new projection-based SVM algorithm with ASVM on a selection of real and synthetic data sets. The new algorithm seems competitive in terms of speed and testing accuracy. 相似文献
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基于内容的图像检索中SVM和Boosting方法集成应用 总被引:2,自引:2,他引:0
提出一种适用于图像内容检索的AdaBoostSVM算法。算法思想是采用支持向量机(SVM)作为AdaBoost算法的分量分类器;基于相关反馈检索机制,通过增加重要样本来模拟AdaBoost算法的权重调整方法。在包含2000幅图像的数据库中进行了检索实验,结果表明AdaBoostSVM算法能有效提高系统的检索性能。 相似文献
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针对网络数据集过于庞大,学习速度过慢的问题,提出了一种基于空间块和样本密度的SVM算法,并将其应用到入侵检测中。该算法根据样本的局部密度选择训练样本,减少参加训练的样本数量,提高学习速度。实验结果表明,该算法在保证检测精度的同时,学习速度快于传统SVM入侵检测方法。 相似文献
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