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检索条件"主题词=AdaBoost algorithm"

共 295 条记录，以下是31-40 订阅

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An Optimized Face Detection Based on adaboost algorithm

An Optimized Face Detection Based on Adaboost Algorithm

引用

International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Zeng Hao Qin Feng Lin Kaidong Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9781538657386

In the real face detection, adaboost based algorithm usually has a higher false positive rate and loss rate. But it faces with the problem of long training time, susceptible to face deflection, obstruction and other factors. In view of the above problems, an improved face detection algorithm is proposed, which can reduce the training time and improve the training speed by using the feature processing, and the detection rate is improved by introducing the skin color detection based on YCgCr color space. Through experimental testing, the proposed algorithm can solve the occlusion, angle, light and other problems to a certain extent.

关键词： adaboost algorithm face detection feature processing skin color detection

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Power Network Vulnerability Detection Based on Improved adaboost algorithm 4th

Power Network Vulnerability Detection Based on Improved Adab...

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4th International Conference on Cloud Computing and Security (ICCCS)

作者： Tao, Wenwei Liu, Song Su, Yang Hu, Chao CSG Power Dispatching Control Ctr Guangzhou 510670 Guangdong Peoples R China NARI Informat & Commun Technol Co Ltd Nanjing 210033 Jiangsu Peoples R China

ISBN: (纸本)9783030000127;9783030000110

The impact of the Internet on the power industry is increasing, the detection of power network vulnerability becomes more and more important. Traditional power network vulnerabilities detection methods are relatively labor-intensive and inefficient, so, the power network vulnerability detection algorithm based on improved adaboost is proposed in this paper. It is a kind of machine learning algorithm, which select C4.5 decision tree as weak classifier to integrate a strong classifier. Compared with neural network, KNN and other methods, the proposed algorithm is more efficient in power network vulnerability detection.

关键词： Machine learning Power network vulnerability detection adaboost algorithm

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An Approach of Malicious Executables Detection on Black & Gray Based on adaboost algorithm

An Approach of Malicious Executables Detection on Black & Gr...

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2nd International Conference on Anti-Counterfeiting, Security and Indentification

作者： Liu, Lei Shao, Kun Hefei Univ Technol HFUT Sch Comp & Informat Hefei Peoples R China

ISBN: (纸本)9781424425846

Behavioral analysis refers to the technique of deciding whether an application is malicious or not, according to what it does. With behavioral analysis research on executables evolving, it is difficult to classify malicious applications and some legal applications called 'gray application', which are classified as malicious sample by 'weak' learners. In theory, boosting can be used to significantly reduce the error of 'weak' learning algorithm that consistently generates classifiers which need only be a little bit better than random guessing. This paper presents an approach based on a new boosting algorithm called adaboost, which improves the performance of any 'weak' learning algorithm. Experiment results show that the method has good classification accuracy in experiment data sets.

关键词： malicious executable malicious host behaviors style adaboost algorithm ROC

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HARDWARE ARCHITECTURE FOR OBJECT DETECTION BASED ON adaboost algorithm

HARDWARE ARCHITECTURE FOR OBJECT DETECTION BASED ON ADABOOST...

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5th International Conference on Computer Vision Theory and Applications (VISAPP 2010)

作者： Xu, Hui Zhao, Feng Ju, Ran Shanghai Jiao Tong Univ Sch Microelect Dongchuan Rd Shanghai Peoples R China

ISBN: (纸本)9789896740290

This paper implements a hardware architecture for object detection based on adaboost learning algorithm and Haar-like features. To increase detection speed and reduce hardware consumption, an integral image calculation array with pipelined feature data flow are introduced. Input images are scanned by sub-windows and detected by cascade classifiers. Moreover, special design is made to enhance the parallelism of the architecture. In comparison with the original design, detection speed is improved by three, with only 5% increase in hardware consumption. The final hardware detection system, implemented on Xilinx V2pro FPGA platform, reaches the detection speed of 80fps and consumes 91% resources of the platform.

关键词： Hardware Object detection adaboost algorithm

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Human action recognition based on adaboost algorithm for feature extraction 14

Human action recognition based on AdaBoost algorithm for fea...

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IEEE International Conference on Computer and Information Technology (CIT 2014)

作者： Ji, Xiaofei Zhou, Lu Li, Yibo Shenyang Aerosp Univ Sch Automat Shenyang Liaoning Peoples R China

ISBN: (纸本)9781479962396

A novel action recognition method based on adaboost algorithm is proposed in this paper. The method can select the most discriminative sample subset from a large amount of raw features of training data, so it can reduce the recognition computational complexity with high accuracy. The histogram of oriented gradient feature (HOG) descriptor is utilized to represent raw feature data. In order to select the most discriminative samples, Gadabouts algorithm is used to extract the raw feature data. The nearest neighbor classifier algorithm is utilized to test the proposed method on the UCF Sports database. Experiment results show that the method not only achieve the better recognition rate but also greatly improve the speed of recognition.

关键词： Histogram of oriented gradient adaboost algorithm Nearest neighbor classifier UCF Sports database

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Eye Detection Based on Improved adaboost algorithm 6

Eye Detection Based on Improved AdaBoost Algorithm

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International Conference on Audio, Language and Image Processing (ICALIP)

作者： Li, Yang Shanghai Jianqiao Univ Coll Informat Technol Shanghai 201306 Peoples R China

ISBN: (纸本)9781538651957

Focused on the issue that the detection error rate of the current eye detection method is relatively high, and when the adaboost algorithm is used to train the classifier, it is easy to appear the phenomenon of weight imbalance. A new eye detection method based on the improved adaboost algorithm is proposed. First, the adaboost algorithm is applied to the detection of human eyes. Then the reason for the imbalance of weights in training of adaboost algorithm is analyzed, and the concept of missing detection rate is introduced to improve the weight updating process of adaboost algorithm. The experimental results show that the improved adaboost algorithm ensured the sample weight distribution balance and improve the accuracy in the training process;eye detection based on the improved adaboost algorithm effectively maintains a high detection rate and inhibits the detection error rate, makes detection more accurate.

关键词： eye detection adaboost algorithm cascade classifier Haar featur intergal image

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SVM ensemble method based on improved iteration processof adaboost algorithm 29

SVM ensemble method based on improved iteration processof Ad...

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第29届中国控制与决策会议

作者： Yiming Tian Xitai Wang School of Control Science and Engineering Hebei University of Technology

ISBN: (纸本)9781509046584

In order to improve the performance of the base classifier in the process of Ada Boost algorithm and simplify the complexity of the whole ensemble learning system, this paper presents a SVM ensemble methodbased on an improved iteration process of adaboost *** improved adaboost algorithm is added with methods of adding sample selection and feature selection in its iterative process in order to solve the problem that adaboost is susceptible to noise and has long training time. First of all, the samples subsets are selected by means of mean nearest neighbor algorithm. Secondly, the feature subset are obtained using the method of relative entropy. Lastly, the individual SVM classifiers are trained by the resulting optimal feature samples subset in each cycle and combined via majority vote to generate the final decision system. The simulation results of UCI datasets show that this algorithm can achieve a higher recognition accuracy on the basis of fewer samples and features compared with the traditional adaboost support vector machine ensemble algorithm.

关键词： ensemble learning adaboost algorithm support vector machine feature selection

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Face Detection Based on Skin Color and adaboost algorithm

Face Detection Based on Skin Color and AdaBoost Algorithm

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Wang, Yu Ban, Xiaojuan Yang, Xing Guo, Yifei Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

Skin color segmentation and adaboost algorithm always play important roles in various face detection methods. To combine the two smoothly, this paper investigates face detection methods based on skin color feature and... 详细信息

ISBN: (纸本)9783038350125

关键词： Face Detection adaboost algorithm Skin Color Segmentation

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Further improvement of adaboost algorithm 7

Further improvement of AdaBoost algorithm

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Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2015)

作者： Liao Shaowen Zhang Jianqing Chen Yong Hexi Univ Informat Technol & Media Inst Zhangye 734000 Gansu Peoples R China Artillery Training Base Gen Staff Xuanhua 075100 Hebei Peoples R China

ISBN: (纸本)9781467371438

adaboost algorithm is a kind of very important feature classification machine learning algorithm, But if difficult samples exist in the training samples, With the iterative Number increasing, this easily leads to degeneration Phenomenon, and reduces the generalization ability of the classifier. In view of the face detection under complex background degeneration appeared problem, This article Proposes LWE-adaboost algorithm which can limit weight expansion, the experimental results indicate that the LWE-adaboost algorithm can restrain the recurrence of degeneration Phenomenon well.

关键词： Face detection adaboost algorithm Integral image Weak classifier Strong classifier Cascaded classifier

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Early Breast Cancer Detection with Digital Mammograms Using Haar-like Features and adaboost algorithm

Early Breast Cancer Detection with Digital Mammograms Using ...

引用

Conference on Sensing and Analysis Technologies for Biomedical and Cognitive Applications

作者： Zheng, Yufeng Yang, Clifford Merkulov, Alex Bandari, Malavika Alcorn State Univ Lorman MS 39096 USA Univ Connecticut Ctr Hlth Farmington CT USA

ISBN: (纸本)9781510601123

The current computer-aided detection (CAD) methods are not sufficiently accurate in detecting masses, especially in dense breasts and/or small masses (typically at their early stages). A small mass may not be perceived when it is small and/or homogeneous with surrounding tissues. Possible reasons for the limited performance of existing CAD methods are lack of multiscale analysis and unification of variant masses. The speed of CAD analysis is important for field applications. We propose a new CAD model for mass detection, which extracts simple Haar-like features for fast detection, uses adaboost approach for feature selection and classifier training, applies cascading classifiers for reduction of false positives, and utilizes multiscale detection for variant sizes of masses. In addition to Haar features, local binary pattern (LBP) and histograms of oriented gradient (HOG) are extracted and applied to mass detection. The performance of a CAD system can be measured with true positive rate (TPR) and false positives per image (FPI). We are collecting our own digital mammograms for the proposed research. The proposed CAD model will be initially demonstrated with mass detection including architecture distortion.

关键词： Computer-aided detection (CAD) Breast cancer detection Haar-like features adaboost algorithm Local binary pattern (LBP) Histograms of oriented gradient (HOG)

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