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strong classifier
相关语句
  强分类器
     The combination of weak classifier strong classifier guarantees the efficiency and effectiveness of the algorithm.
     这种基于两阶段分类的模型有效地结合了弱分类器与强分类器,使算法效率和有效性同时得以保证。
短句来源
     On the basis of the Haar-Like feature,AdaBoost algorithm was adopted to produce the strong classifier for face detection.
     采用AdaBoost算法对人脸图像的Haar-Like特征进行统计学习,生成用于人脸检测的强分类器
短句来源
     In view of this, we propose a real-time face detection algorithm based on skin color model verification and the Haar-like features cascade strong classifier (SCC-HCC) in the third chapter.
     鉴于此,论文第三章提出了基于肤色模型校验和Haar特征级联强分类器的快速人脸检测算法(SCC-HCC)。
短句来源
     Then Based on the concept of IntraPersonal and ExtraPersonal space, AdaBoost is taken to select the effective Gabor features and to build the strong classifier.
     第二步介绍了本课题的工作重点,基于IntraPersonal,ExraPersonal空间概念,利用AdaBoost对Gabor特征进行选择并且构造强分类器
短句来源
     Learning algorithm chooses and combines the weak classifier, generated the strong classifier combined by weak classifiers, and then it can be used to detect face.
     这种方法首先定义一种特征形式并基于此特征形式使用构造分类器的方法构造出可以进行初步人脸检测的多个弱分类器,这些弱分类器经过学习算法的挑选和组合,作为学习算法的输入生成强分类器,进行人脸检测。
短句来源
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  “strong classifier”译为未确定词的双语例句
     Finally, we designed an erotic image scanning system adopting the thought that many connecting weak classifiers can acquire a strong classifier, which is used to test the method of filtering and the key technique, and is an experimental frame system.
     最后,我们采用将多个弱分类器串联的思想设计实现了一个单机版的敏感图像扫描原型系统,主要作为各种基于内容的图像过滤算法及关键技术的测试之用,是一个实验性的框架系统。
短句来源
     2. Four types of unsymmetrical rectangle filters are given, which can improve the discriminability in last scale and decrease the numbers of feature in the strong classifier dramatically.
     2.提出了四类非对称矩形特征,改善了矩形特征在小尺度上可分辨性;
短句来源
     The new method could limit the false positive rate or false negative rate to a certain threshold and meet the special needs of the strong classifier in a cascade classifier.
     在此基础上,又提出了一种阈值自适应的权重更新方法,能根据实际需要调整正样本或负样本错分率于预期的阈值之内。
短句来源
     Cascade AdaBoost algorithm was used for seeking the candidate face in the target regions: The AdaBoost algorithm will combine a strong classifier through many weak classifiers, to obtain a classifier for one feature.
     采用Cascade AdaBoost算法在目标区域中寻找候选人脸:AdaBoost算法通过将多个弱分类器组合成一个强的分类器,得到判断人脸中某一特征的分类器;
  相似匹配句对
     The orientation of classifier phrase is strong.
     量词短语的定位性较强。
短句来源
     THE BONE IS STRONG
     岁月流逝 骨不更年
短句来源
     strong positive.
     强阳性(++),积分>4分。
短句来源
     The combination of weak classifier strong classifier guarantees the efficiency and effectiveness of the algorithm.
     这种基于两阶段分类的模型有效地结合了弱分类器与强分类器,使算法效率和有效性同时得以保证。
短句来源
     Preprocessing Data for Classifier
     分类器的数据预处理
短句来源
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  strong classifier
We describe a novel variant of the AdaBoost based learning algorithm, which builds a strong classifier by incremental addition of weak classifiers (WCs) that minimize the combined error of the already selected WCs.
      
Weak classifiers are learned and combined as weighted-sum to form a strong classifier as one layer of the detector.
      
We assume that of these features, only a small number are necessary to form an effective strong classifier.
      
The goal is to construct a strong classifier that achieves much higher accuracy than the individual weak classifiers.
      
The final strong classifier is a linear combination of these weak classifiers.
      
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For HMM models are usually applied of lip-reading systems presently, a lip-reading recognition method based on AdaBoost is proposed in this paper. This method solves the overlap problem of sample space, and improves the performance of recognition by strengthening training the samples that are difficult to be classified correctly. The method mentioned above, improves the change rate of the samples' weights in the iteration process, reduces the updating speed of samples' weights; and it can distinguish noisy samples,...

For HMM models are usually applied of lip-reading systems presently, a lip-reading recognition method based on AdaBoost is proposed in this paper. This method solves the overlap problem of sample space, and improves the performance of recognition by strengthening training the samples that are difficult to be classified correctly. The method mentioned above, improves the change rate of the samples' weights in the iteration process, reduces the updating speed of samples' weights; and it can distinguish noisy samples, and reduces the weights of unreasonable weak classifiers, so that the improved algorithm suffers less influence of noisy data on strong classifier.

针对目前唇读系统多采用HMM的识别方法,提出了基于AdaBoost的唇读识别方法,有效地解 决了样本空间的交叠问题,通过强化训练那些难以分类的样本,使得识别性能有所提高.该方法改进 了迭代过程中权值的变化率,降低了样本权重更新速度;同时区分噪声样本,减小不合理弱分类器的 权重,使得改进后的算法降低了噪声对强分类器的影响.

Aiming at the problem that training time of Adaboost face detection is extremely long, two improvement methods were proposed:One method was to directly solve the parameter of single weaker classifier,the other was to introduce a double threshold decision to make stronger classifier.The experiment results show that the number of weaker classifiers needed in Adaboost face detection system updated is dramatically reduced and its training speed is about 11 times higher than that of the traditional method....

Aiming at the problem that training time of Adaboost face detection is extremely long, two improvement methods were proposed:One method was to directly solve the parameter of single weaker classifier,the other was to introduce a double threshold decision to make stronger classifier.The experiment results show that the number of weaker classifiers needed in Adaboost face detection system updated is dramatically reduced and its training speed is about 11 times higher than that of the traditional method.

针对Adaboost人脸检测训练非常耗时的问题,从训练中直接求解目标函数和弱分类器使用双阈值判决构造强分类器两个方面对人脸检测系统进行了改进。实验结果表明,改进后的系统使用的弱分类器数目大大减少,并且训练速度比传统方法高11倍左右。

In this paper,we present two novel approaches for gender classification by local binary pattern(LBP) based classifiers.The first one is cascade LBP method.In this method,we apply wavelet to decompose images into four kinds of frequency images.Then we extract LBP features with the local to holistic way to make features more representative.And,the adaptive weight mechanism is adopted to show the different importance of feature data.The second one is the boosting LBP method,in which the face area is scanned with...

In this paper,we present two novel approaches for gender classification by local binary pattern(LBP) based classifiers.The first one is cascade LBP method.In this method,we apply wavelet to decompose images into four kinds of frequency images.Then we extract LBP features with the local to holistic way to make features more representative.And,the adaptive weight mechanism is adopted to show the different importance of feature data.The second one is the boosting LBP method,in which the face area is scanned with scalable small windows from which LBP histograms are obtained to effectively express the local feature of a face image.The Chi square distance between corresponding LBP histograms of sample image and template is used to construct weak classifiers pool.Adaboost algorithm is applied to build the final strong classifier by selecting and combining the most useful weak classifiers.In addition,several experiments are made for classifying gender based on local binary pattern.The male and female image set is collected from FERET database.Finally,the results of experiments show that the features extracted by LBP operator are discriminative for gender classification and our proposed approaches achieve better performance of classification than several other methods.

提出了两种基于局部二元模式(Local Binary Pattern,LBP)算子的人脸性别分类方法:级联LBP方法和boosting LBP方法.前一种方法遵循从局部到整体的级联策略,使用LBP算子对由小波分解得到的细节图像进行特征提取,以达到扩充特征提取范围和增强所提取特征的有效性的目的,随后采用自适应加权机制对人脸图像的各个分块赋以不同的权值.后一种方法采用可变尺寸的子窗口对人脸图像进行扫描,在扫描所得的每个子窗口中,使用LBP算子对该子窗口提取LBP直方图.计算样本图像的LBP直方图和模板的LBP直方图之间的度量,并由此构建弱分类器集.利用Adaboost算法选取最有效的若干个弱分类器集组合成为强分类器.进行了三个基于LBP算子的人脸性别分类实验,实验所使用的训练集和测试集皆选自FERET人脸数据库.实验结果证明:LBP算子能有效地从人脸图像中提取出针对人脸性别分类的特征,并可以达到人脸性别分类的目的.所提出的两种基于LBP算子的方法可以有效的解决传统LBP方法所存在的特征提取范围有限、加权机制客观性不足等问题.

 
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