检索结果-内蒙古大学图书馆

Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques

PATTERN RECOGNITION 2009年第9期42卷 1859-1868页

作者： Tie, Yun Guan, Ling Ryerson Univ Ryerson Multimedia Res Lab Toronto ON Canada

Automatic human face detection from video sequences is an important component of intelligent human computer interaction systems for video surveillance, face recognition, emotion recognition and face database management. This paper proposes an automatic and robust method to detect human faces from video sequences that combines feature extraction and face detection based on local normalization, Gabor wavelets transform and adaboost algorithm. The key step and the main contribution of this work is the incorporation of a normalization technique based on local histograms with optimal adaptive correlation (OAC) technique to alleviate a common problem in conventional face detection methods: inconsistent performance due to sensitivity to variation illuminations such as local shadowing, noise and occlusion. The approach uses a cascade of classifiers to adopt a coarse-to-fine strategy for achieving higher detection rates with lower false positives. The experimental results demonstrate a significant performance improvement gains and achieved by local normalization over methods without normalizations in real video sequences with a wide range of facial variations in color, position, scale, and varying lighting conditions. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Local normalization Optimal adaptive correlation (OAC) adaboost algorithm

来源：评论

学校读者我要写书评

暂无评论

MULTISCALE DYNAMIC FEATURES BASED DRIVER FATIGUE DETECTION

引用

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2009年第3期23卷 575-589页

作者： Yin, Bao-Cai Fan, Xiao Sun, Yan-Feng Beijing Univ Technol Beijing Key Lab Multimedia & Intelligent Software Coll Comp Sci & Technol Beijing 100124 Peoples R China

Driver fatigue is a significant factor in many traffic accidents. We propose a novel approach for driver fatigue detection from facial image sequences, which is based on multiscale dynamic features. First, Gabor filters are used to get a multiscale representation for image sequences. Then Local Binary Patterns are extracted from each multiscale image. To account for the temporal aspect of human fatigue, the LBP image sequence is divided into dynamic units, and a histogram of each dynamic unit is computed and concatenated as dynamic features. Finally a statistical learning algorithm is applied to extract the most discriminative features from the multiscale dynamic features and construct a strong classifier for fatigue detection. The proposed approach is validated under real-life fatigue conditions. The test data includes 600 image sequences with illumination and pose variations from 30 people's videos. Experimental results show the validity of the proposed approach, and a correct rate of 98.33% is achieved which is much better than the baselines.

关键词： Human fatigue Gabor filters Local Binary Pattern (LBP) adaboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Efficient Improvement for adaboost based object detection

Efficient Improvement for Adaboost based object detection

引用

2009 International Conference on Computational Intelligence and Natural Computing(CINC 2009)

作者： Luo Sheng, Ye Xin-quan School of Mechanical & Electrical Engineering, Wenzhou University, Zhejiang Province, China

Object detection by the traditional adaboost algorithm is very time-consuming mainly because the candidate feature number is large, and the feature number in the final strong detector is large. So this paper elevates 3 efficient optimization techniques and implements to reduce the training time. First we use some preprocessing technique to reduce the candidate features size to ten percent of the original, and then we use some implement skills to further reduce the training time. Besides these, we use double thresholds to describe each feature, which can improve the efficient of each feature, and reduce the required feature number for the final strong classifier. The experiment result show that the training of our system is hundred time faster than previous systems.

关键词： Object detection adaboost algorithm Optimization techniques Double thresholds

来源：评论

学校读者我要写书评

暂无评论

Using Chou's pseudo amino acid composition based on approximate entropy and an ensemble of adaboost classifiers to predict protein subnuclear location

引用

AMINO ACIDS 2008年第4期34卷 669-675页

作者： Jiang, Xiaoying Wei, Rong Zhao, Yanjun Zhang, Tongliang Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Henan Inst Sci & Technol Sch Chem & Chem Engn Xinxiang 453003 Henan Peoples R China Hebei Polytech Univ Coll Sci Tangshan 063009 Hebei Peoples R China

The knowledge of subnuclear localization in eukaryotic cells is essential for understanding the life function of nucleus. Developing prediction methods and tools for proteins subnuclear localization become important research fields in protein science for special characteristics in cell nuclear. In this study, a novel approach has been proposed to predict protein subnuclear localization. Sample of protein is represented by Pseudo Amino Acid (PseAA) composition based on approximate entropy (ApEn) concept, which reflects the complexity of time series. A novel ensemble classifier is designed incorporating three adaboost classifiers. The base classifier algorithms in three adaboost are decision stumps, fuzzy K nearest neighbors classifier, and radial basis-support vector machines, respectively. Different PseAA compositions are used as input data of different adaboost classifier in ensemble. Genetic algorithm is used to optimize the dimension and weight factor of PseAA composition. Two datasets often used in published works are used to validate the performance of the proposed approach. The obtained results of Jackknife cross-validation test are higher and more balance than them of other methods on same datasets. The promising results indicate that the proposed approach is effective and practical. It might become a useful tool in protein subnuclear localization. The software in Matlab and supplementary materials are available freely by contacting the corresponding author.

关键词： protein subnuclear localization pseudo amino acid composition approximate entropy ensemble classifier adaboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Improving Svm Learning Accuracy with adaboost

Improving Svm Learning Accuracy with Adaboost

引用

4th International Conference on Natural Computation (ICNC 2008)

作者： Zhang, Xiaolong Ren, Fang Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430081 Peoples R China

ISBN: (纸本)9780769533049

Support vector machine (SVM) is based on the VC theory and the principle of structural risk minimization. For some learning domains that need more accurate learning performance, SVM can be improved for this objective. This paper describes an algorithm-Boost-SVM, which puts SVM into adaboost framework to improve the learning accuracy of the SVM algorithm. By changing the weights of the training examples in the re-sampling process of adaboost, SVM appears to be more accurate. The experimental results show that the proposed method has a competitive learning ability and acquires better accuracy than SVM.

关键词： SVM Boosting algorithm adaboost algorithm

来源：评论

学校读者我要写书评

暂无评论

An Approach of Multiple Classifiers Ensemble Based on Feature Selection

An Approach of Multiple Classifiers Ensemble Based on Featur...

引用

5th International Conference on Fuzzy Systems and Knowledge Discovery

作者： Chen, Bing Zhang, Hua-Xiang Shandong Normal Univ Coll Informat Sci & Engn Jinan 250014 Peoples R China

ISBN: (纸本)9780769533056

In order to improve the classification performance of classifiers, an approach of multiple classifiers ensemble based on feature selection (FSCE) is proposed in the paper. After attributes of the training data set are specially selected, the new data set is mapped into new training data sets. There is the number of attributes (the class attribute is ignored) of the new data sets. Then classifiers with better performance are selected from the classifiers that are trained in every small training data set. They are used to classify the corresponding small testing data sets that are disposed by attribute selection. FSCE is tested on the UCI benchmark data sets, and compared classification efficiency with member classifiers trained based on the algorithm of adaboost. In this way, the utility of FSCE can be proved in the paper.

关键词： classifier ensemble feature selection SVM adaboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Dialect/accent classification using unrestricted audio

引用

IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2007年第2期15卷 453-464页

作者： Huang, Rongqing Hansen, John H. L. Angkititrakul, Pongtep Univ Texas Dept Elect Engn Ctr Robust Speech Syst Richardson TX 75083 USA

This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based system with significantly less computational costs. The new algorithm, which is named Word-based Dialect Classification (WDC), converts the text-independent decision problem into a text-dependent decision problem and produces multiple combination decisions at the word level rather than making a single decision at the utterance level. The basic WDC algorithm also provides options for further modeling and decision strategy improvement. Two sets of classifiers are employed for WDC: a word classifier D-W(k) and an utterance classifier D-u. D-W(k) is boosted via the adaboost algorithm directly in the probability space instead of the traditional feature space. D. is boosted via the dialect dependency information of the words. For a small training corpus, it is difficult to obtain a robust statistical model for each word and each dialect. Therefore, a context adapted training (CAT) algorithm is formulated, which adapts the universal phoneme Gaussian mixture models (GMMs) to dialect-dependent word hidden Markov models (HMMs) via linear regression. Three separate dialect corpora are used in the evaluations that include the Wall Street Journal (American and British English), NATO N4 (British, Canadian, Dutch, and German accent English), and IME (eight British dialects). Significant improvement in dialect classification is achieved for all corpora tested.

关键词： accent/dialect classification adaboost algorithm context adapted trianing dialect dependency information limited training data robust acoustic modeling word-based modeling

来源：评论

学校读者我要写书评

暂无评论

Pedestrian detection with local feature assistant

Pedestrian detection with local feature assistant

引用

IEEE International Conference on Control and Automation

作者： Xu, Y. W. Cao, X. B. Qiao, H. Univ Sci & Technol China Dept Comp Sci & Technol Hefei 230026 Peoples R China Anhui Prov Key Lab Software Comp & Commun Hefei 230026 Peoples R China Chinese Acad Sci Inst Automat Beijing 10080 Peoples R China

ISBN: (纸本)9781424408177

Until now, existing pedestrian detection systems usually use global features (e.g. appearance or motion) of human body to detect pedestrian;however, the detection rate needs to be improved in many situations since sometimes the global features can not be obtained. For example, a pedestrian may be partly covered by a car or his/her part may hide into the background. Therefore it is essential to adopt some local features of key parts of human body to assist pedestrian detection. In this paper, we propose a method using some key local features of human body to help pedestrian detection. Since the introduction of additional features will cost the system more time, in order to ensure the detection speed, we firstly use both appearance and motion global features of human body to select candidates, and then use local features of head and leg to do further confirmation. In the confirmation stage, we use three kinds of local features (head appearance, face color and hair color) to detect the head of each candidate;at the same time, we also choose some particular local appearance features to detect the leg. The experimental results indicate that this method can improve detection rate with almost the same detection speed;additionally, it can reduce false alarm sometimes.

关键词： pedestrian detection local feature adaboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Eye location using hierarchical classifier

Eye location using hierarchical classifier

引用

6th International Conference on Machine Learning and Cybernetics

作者： Xu, Gao-Feng Huang, Lei Liu, Chang-Ping Ding, Shi-Qi Harbin Engn Univ Harbin 150001 Heilongjiang Peoples R China Chinese Acad Sci Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9781424409723

The eyes are important facial landmarks, both for image normalization due to their relatively constant interocular distance, and for post processing due to their anchoring model-based schemes. In this paper a hierarchical classifier for precise eye location algorithm is introduced. The new algorithm involves three main steps. First, a classifier based on adaboost algorithm is introduced to locate eye pair roughly. In order to get a reliable location, simple eye location is also introduced for checking the located results. Second, a precise eye location method based on Minimum Extreme Region (MER) is employed to detect eye candidates. Finally, a combine processing based on the above two results is used for the last location. The algorithm is tested on database CAS-PEAL, JAFFE and BiolD. The results prove the merit of this approach.

关键词： eye location adaboost algorithm minimum extreme region face recognition

来源：评论

学校读者我要写书评

暂无评论

EYE LOCATION USING HIERARCHICAL CLASSIFIER

EYE LOCATION USING HIERARCHICAL CLASSIFIER

引用

2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： GAO-FENG XU SHI-QI DING CHANG-PING LIU LEI HUANG Harbin Engineering University Harbin Heilongjiang 150001 China Institute of Automation Chinese Academy of Sciences 100083 China

The eyes are important facial landmarks, both for image normalization due to their relatively constant interocular distance, and for post processing due to their anchoring model-based *** this paper a hierarchical classifier for precise eye location algorithm is *** new algorithm involves three main ***, a classifier based on adaboost algorithm is introduced to locate eye pair *** order to get a reliable location, simple eye location is also introduced for checking the located ***, a precise eye location method based on Minimum Extreme Region (MER) is employed to detect eye ***, a combine processing based on the above two results is used for the last *** algorithm is tested on database CAS-PEAL, JAFFE and *** results prove the merit of this approach.

关键词： Eye location adaboost algorithm Minimum extreme region Face recognition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：