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作者： Krolikowski, Martin Brno University of Technology

Diplomová práce se zabývá problémem detekce obličejů v barevných statických obrazech. V práci jsou nastíněny základní pojmy, se kterými se lze při detekci o... 详细信息

Diplomová práce se zabývá problémem detekce obličejů v barevných statických obrazech. V práci jsou nastíněny základní pojmy, se kterými se lze při detekci obličeje setkat, a jejich vzájemné souvislosti. Jednotlivé přístupy k řešení problému detekce obličeje jsou rozděleny do skupin a blíže popsány. Z těchto přístupů se práce detailně zabývá algoritmem adaboost, jenž byl vybrán pro jeho pozitivní vlastnosti, kterými jsou zejména rychlost a dobré dosažené výsledky. V rámci práce byl implementován Viola-Jones detektor. Tento detektor byl natrénován na veřejně přístupné databázi obličejových obrazů a byla zkoumána možnost jeho kombinace s jednoduchým detektorem barvy kůže. Další oblastí, kterou se práce zabývá, je experimentální detekce určitých rysů obličeje.

关键词： Detekce obličeje Viola-Jones detektor algoritmus adaboost detektor barvy kůže klasifikace strojové učení Face detection Viola-Jones detector adaboost algorithm face color detector classification machine learning Text

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Learning features from examples for face detection

Learning features from examples for face detection

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Lu, XF Zheng, SF Zheng, NN Liu, WX Xian Jiaotong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

ISBN: (纸本)0780376633

In this paper, LPSVM algorithm are used to construct an over complete set of weak classifiers, and adaboost algorithm are adopted to select part of them to form a strong classifier. During the course of feature extraction and selection, the new method can minimize the classification error directly, whereas most previous works cannot do this. An important difference between this method and other methods is that the sparse features are learnt from the training set instead of being arbitrarily defined. Experiments demonstrate that the new algorithm performs well.

关键词： face recognition feature extraction learning automata image classification features learning face detection LPSVM algorithm linear support vector machine weak classifiers adaboost algorithm strong classifier feature extraction feature selection classification error minimization training set pattern recognition image scanning

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Combined Prediction Energy Model at Software Architecture Level

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IEEE ACCESS 2020年 8卷 214565-214576页

作者： Li, Junke Liu, Kai Li, Mingjiang Li, Deguang Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Peoples R China Luoyang Normal Univ Sch Informat Luoyang 471934 Peoples R China

Accurate prediction of software energy consumption is of great significance for the sustainable development of the environment. In order to overcome the limitations of a single prediction method and further improve the prediction accuracy, a combined prediction energy model of adaboost algorithm and RBF (radial basis function) neural network at software architecture level is proposed. Firstly, three kinds of energy prediction models are established by polynomial regression, support vector machine and neural network respectively. Secondly, the RBF neural network is used to nonlinear combine the predicted values of the above three models. Finally, RBF integrated by adaboost algorithm is used as high-precision prediction of energy consumption. Experimental results show that the prediction accuracy of the combined prediction model is higher than that of the single model.

关键词： Software Energy consumption Predictive models Power demand Computer architecture Hardware Estimation Green computing energy consumption prediction adaboost algorithm RBF neural network combined prediction model

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Video captioning using boosted and parallel Long Short-Term Memory networks

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COMPUTER VISION AND IMAGE UNDERSTANDING 2020年 190卷 102840-000页

作者： Nabati, Masoomeh Behrad, Alireza Shahed Univ Dept Elect Engn Tehran *** Iran

Video captioning and its integration with deep learning is one of the most challenging issues in the field of machine vision and artificial intelligence. In this paper, a new boosted and parallel architecture is proposed for video captioning using Long Short-Term Memory (LSTM) networks. The proposed architecture comprises two LSTM layers and a word selection module. The first LSTM layer has the responsibility of encoding frame features extracted by a pre-trained deep Convolutional Neural Network (CNN). In the second LSTM layer, a novel architecture is used for video captioning by leveraging several decoding LSTMs in a parallel and boosting architecture. This layer, which is called Boosted and Parallel LSTM (BP-LSTM) layer, is constructed by iteratively training LSTM networks using a special kind of adaboost algorithm during the training phase. During the testing phase, the outputs of BP-LSTMs are concurrently combined using the maximum probability criterion and word selection module. We tested the proposed algorithm with two well-known video captioning datasets and compared the results with state-of-the-art algorithms. The results show that the proposed architecture considerably improves the accuracy of the generated sentence.

关键词： Video captioning Boosted and parallel LSTMs adaboost algorithm

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Improved classification approach for use with large-scale scene images in the Hadoop cluster environment

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JOURNAL OF ELECTRONIC IMAGING 2018年第6期27卷 063027-063027页

作者： Cao, Jianfang Li, Yanfei Wang, Yan Cui, Hongyan Tian, Yun Xinzhou Teachers Univ Dept Comp Sci & Technol Xinzhou Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Shanxi Peoples R China

Faced with massive amounts of image data, the performance of classification algorithms based on traditional platforms with single-node architecture drops dramatically. We propose a classification method based on hybrid optimization and combination technology in a cluster environment that is suitable for use with large-scale scene images. Support vector machine (SVM) algorithms are optimized by the artificial bee colony and particle swarm optimization algorithms to produce weak classifiers;then, a strong classifier is constructed by combining the outputs from the 15 weak classifiers using the adaboost algorithm. The MapReduce parallel programming model in the Hadoop platform is used to parallelize the algorithm, and a parallel adaboost hybrid optimization (PAH)-SVM algorithm is proposed. Finally, a model is constructed for automatic classification of the large-scale scene images. Multiple sets of comparative experiments show that the average classification accuracy of the proposed algorithm when applied to the scene understanding (Caltech-256 and Pascal VOC 2012) database exceeds 85.0%, and its training time is <10 min when 170,000 images are used. Considering the cost of hardware, the execution time and accuracy of this algorithm are superior to those of mainstream classification algorithms, such as P-SVM and CNN. In addition, the speed of the system based on the proposed algorithm increases linearly, and the constructed Hadoop cluster shows good extensibility. The proposed algorithm is suitable for automatic classification and prediction using large-scale scene images. (C) 2018 SPIE and IS&T

关键词： hybrid optimization adaboost algorithm cluster environments MapReduce

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Collaboration graph for feature set partitioning in data classification

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartB期213卷

作者： Taheri, Khalil Moradi, Hadi Tavassolipour, Mostafa Univ Tehran Coll Engn Sch Elect & Comp Engn Machine Intelligence & Robot Dept Tehran 111554563 Iran SKKU Intelligent Syst Res Inst ISRI Suwon 16419 South Korea

The curse of dimensionality of features in data classification is still an open issue. An approach to solve this problem is to partition features into several sub-sets of features hence the data classification task for every subset is performed. Then, an ensemble of these classifications are reported as the result of the classification problem. However, the feature set partitioning into sub-sets of features is still an area of research interest. Thus, in this paper, an innovative framework is proposed in which, first, a collaboration measure between each two features is defined and measured. Then, the collaboration graph, consisted of features as nodes and measured collaborations as edges' weights, is generated according to the collaboration measures calculated. After that, a community detection method is used to find the graph communities. The communities are considered as the feature subsets and a base classifier is trained for each subset based on the corresponding training data of the subsets. Then, the ensemble classifier is created by a combination of base classifiers according to the adaboost Aggreagation. The simulation results of the proposed approach over the real and synthetic datasets indicate that the proposed approach considerably increases the classification accuracy in comparison to previous methods.

关键词： Ensemble Classification Features Collaboration Graph Community Detection adaboost algorithm

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Fatigue Driving Detection Method Based on Combination of BP Neural Network and Time Cumulative Effect

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SENSORS 2022年第13期22卷 4717-4717页

作者： Chen, Jian Yan, Ming Zhu, Feng Xu, Jing Li, Hai Sun, Xiaoguang Yangzhou Univ Sch Mech Engn Yangzhou 225127 Jiangsu Peoples R China

Fatigue driving has always received a lot of attention, but few studies have focused on the fact that human fatigue is a cumulative process over time, and there are no models available to reflect this phenomenon. Furthermore, the problem of incorrect detection due to facial expression is still not well addressed. In this article, a model based on BP neural network and time cumulative effect was proposed to solve these problems. Experimental data were used to carry out this work and validate the proposed method. Firstly, the adaboost algorithm was applied to detect faces, and the Kalman filter algorithm was used to trace the face movement. Then, a cascade regression tree-based method was used to detect the 68 facial landmarks and an improved method combining key points and image processing was adopted to calculate the eye aspect ratio (EAR). After that, a BP neural network model was developed and trained by selecting three characteristics: the longest period of continuous eye closure, number of yawns, and percentage of eye closure time (PERCLOS), and then the detection results without and with facial expressions were discussed and analyzed. Finally, by introducing the Sigmoid function, a fatigue detection model considering the time accumulation effect was established, and the drivers' fatigue state was identified segment by segment through the recorded video. Compared with the traditional BP neural network model, the detection accuracies of the proposed model without and with facial expressions increased by 3.3% and 8.4%, respectively. The number of incorrect detections in the awake state also decreased obviously. The experimental results show that the proposed model can effectively filter out incorrect detections caused by facial expressions and truly reflect that driver fatigue is a time accumulating process.

关键词： fatigue driving detection adaboost algorithm cascade regression tree BP neural network algorithm time accumulation effect

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Classification of mammogram using two-dimensional discrete orthonormal S-transform for breast cancer detection

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HEALTHCARE TECHNOLOGY LETTERS 2015年第2期2卷 46-51页

作者： Beura, Shradhananda Majhi, Banshidhar Dash, Ratnakar Roy, Susnata Natl Inst Technol Dept Comp Sci & Engn Pattern Recognit Lab Rourkela 769008 India

An efficient approach for classification of mammograms for detection of breast cancer is presented. The approach utilises the two-dimensional discrete orthonormal S-transform (DOST) to extract the coefficients from the digital mammograms. A feature selection algorithm based the on null-hypothesis test with statistical 'two-sample t-test' method has been suggested to select most significant coefficients from a large number of DOST coefficients. The selected coefficients are used as features in the classification of mammographic images as benign or malignant. This scheme utilises an adaboost algorithm with random forest as its base classifier. Two standard databases Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) are used for the validation of the proposed scheme. Simulation results show an optimal classification performance with respect to accuracies of 98.3 and 98.8% and AUC (receiver operating characteristic) values of 0.9985 and 0.9992 for MIAS and DDSM, respectively. Comparative analysis shows that the proposed scheme outperforms its competent schemes.

关键词： adaboost algorithm Digital Database for Screening Mammography database Mammographic Image Analysis Society database breast cancer detection cancer feature extraction feature selection image classification learning (artificial intelligence) mammogram classification mammography medical image processing null-hypothesis test statistical testing statistical two-sample t-test method two-dimensional discrete orthonormal S-transform

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APO-ELM Model for Improving Azimuth Correction of Shipborne HFSWR

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REMOTE SENSING 2023年第15期15卷 3818-3818页

作者： Wang, Yaning Yu, Haibo Zhang, Ling Li, Gangsheng Ocean Univ China Coll Engn Qingdao 266100 Peoples R China Ocean Univ China Fundamental Comp Dept Qingdao 266100 Peoples R China Ocean Univ China Dept Educ Qingdao 266100 Peoples R China

Shipborne high-frequency surface wave radar (HFSWR) has a wide range of applications and plays an important role in moving target detection and tracking. However, the complexity of the sea detection environment causes the target signals received by shipborne HFSWR to be seriously disturbed by sea clutter. Sea clutter increases the difficulty of azimuth estimation, resulting in a challenging problem for shipborne HFSWR. To solve this problem, a novel azimuth correction method based on adaptive boosting error feedback dynamic weighted particle swarm optimization extreme learning machine (APO-ELM) is proposed to improve the azimuth estimation accuracy of shipborne HFSWR. First, the sea clutter is modeled and simulated. Then, we study its characteristics and analyze the influence of its characteristics on the first-order clutter spectrum and target detection accuracy, respectively. In addition, the proposed improved particle swarm optimization (PSO) and adaptive neuron clipping algorithm are used to optimize the input parameters of the ELM network. Then, the network performs error feedback based on the optimized parameter performance and updates the feature matrix, which can give a minimum clutter-error estimation. After that, it iteratively trains multiple weak learners using the adaptive boosting (adaboost) algorithm to form a strong learner and make strong predictions. Finally, after error compensation, the best azimuth estimation results are obtained. The sample sets used for the APO-ELM network are obtained from field shipborne HFSWR data. The network training and testing features include the wind direction, sea current, wind speed, platform speed, and signal-to-clutter ratio (SCR). The experimental results show that this method has a lower root-mean-square error than the back-propagation neural network and support vector regression (SVR) azimuth correction methods, which verifies the effectiveness of the proposed method.

关键词： azimuth correction sea clutter extreme learning machine (ELM) high-frequency surface wave radar (HFSWR) adaptive boosting error feedback dynamically weighted particle swarm optimization (APO) neuron cropping adaboost algorithm

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A Method for Automatic Segmentation of Nuclei in Phase-Contrast Images Based on Intensity, Convexity and Texture

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2014年第5期8卷 716-728页

作者： Dewan, M. Ali Akber Ahmad, M. Omair Swamy, M. N. S. Concordia Univ Dept Elect & Comp Engn Ctr Signal Proc & Commun Montreal PQ H3G 1M8 Canada

This paper presents a method for automatic segmentation of nuclei in phase-contrast images using the intensity, convexity and texture of the nuclei. The proposed method consists of three main stages: preprocessing, -maxima transformation- based marker controlled watershed segmentation (-TMC), and texture analysis. In the preprocessing stage, a top-hat filter is used to increase the contrast and suppress the non-uniform illumination, shading, and other imaging artifacts in the input image. The nuclei segmentation stage consists of a distance transformation, -maxima transformation and watershed segmentation. These transformations utilize the intensity information and the convexity property of the nucleus for the purpose of detecting a single marker in every nucleus;these markers are then used in the -TMC watershed algorithm to obtain segments of the nuclei. However, dust particles, imaging artifacts, or prolonged cell cytoplasm may falsely be segmented as nuclei at this stage, and thusmay lead to an inaccurate analysis of the cell image. In order to identify and remove these non-nuclei segments, in the third stage a texture analysis is performed, that uses six of the Haralick measures along with the adaboost algorithm. The novelty of the proposed method is that it introduces a systematic framework that utilizes intensity, convexity, and texture information to achieve a high accuracy for automatic segmentation of nuclei in the phase-contrast images. Extensive experiments are performed demonstrating the superior performance (precision = 0.948;recall = 0.924;F-1- measure = 0.936;validation based on similar to 4850 manually-labelled nuclei) of the proposed method.

关键词： adaboost algorithm Haralick features nuclei clustering phase-contrast image segmentation of nuclei

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