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Facial expression recognition via weighted group sparsity

Frontiers of Computer Science 2017年第2期11卷 266-275页

作者： Hao ZHENG Xin GENG Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing Xiaozhuang University Nanjing 211171 China School of Computer Science and Engineering Southeast University Nanjing 211189 China

Considering the distinctiveness of different group features in the sparse representation, a novel joint multi- task and weighted group sparsity （JMT-WGS） method is pro- posed. By weighting popular group sparsity, not only the rep- resentation coefficients from the same class over their asso- ciate dictionaries may share some similarity, but also the rep- resentation coefficients from different classes have enough di- versity. The proposed method is cast into a multi-task frame- work with two-stage iteration. In the first stage, representa- tion coefficient can be optimized by accelerated proximal gra- dient method when the weights are fixed. In the second stage, the weights are computed via the prior information about their entropy. The experimental results on three facial expres- sion databases show that the proposed algorithm outperforms other state-of-the-art algorithms and demonstrate the promis- ing performance of the proposed algorithm.

关键词： facial expression recognition multi-task learning group sparsity

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Unsupervised feature selection based on Markov blanket and particle swarm optimization

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Journal of Systems Engineering and Electronics 2017年第1期28卷 151-161页

作者： Yintong Wang Jiandong Wang Hao Liao Haiyan Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University College of Computer Science and Software Engineering Shenzhen University

Feature selection plays an important role in data mining and recognition, especially in the large scale text, image and biological data. Specifically, the class label information is unavailable to guide the selection of minimal feature subset in unsupervised feature selection, which is challenging and interesting. An unsupervised feature selection based on Markov blanket and particle swarm optimization is proposed named as UFSMB-PSO. The proposed method seeks to find the high-quality feature subset through multi-particles' cooperation of particle swarm optimization without using any learning algorithms. Moreover, the features' relevance will be computed based on an information metric of relevance gain, which provides an information theoretical foundation for finding the minimization of the redundancy between features. Our results on several benchmark datasets demonstrate that UFSMB-PSO can achieve significant improvement over state of the art unsupervised methods. © 1990-2011 Beijing Institute of Aerospace Information.

关键词： Character recognition data mining Feature extraction Information theory

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The research of human interaction recognition based on fusion features of key frame feature library

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International Journal of Information and Communication Technology 2021年第1期18卷 57-69页

作者： Zhang, Haiyan Gao, Shangbing Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China Nanjing Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing China

Some issues such as computational complexity and low recognition accuracy still exist in human interaction recognition. In order to solve the problem, the paper has proposed innovative and effective method based on fixed features of key frame feature library. Firstly, GIST feature and HOG feature were extracted from the pre-processed videos. Secondly, training videos with different kinds of actions were clustered by the K-means algorithm respectively to get key frame feature of each action for constructing key frame feature library. And similarity measure was utilised to calculate the frequency of different key frames in every interactive video, and statistical histogram representation of interactive videos were obtained. Finally, the decision level fusion was achieved by using SVM classifier based on histogram intersection kernel to obtain impressive results on UT-interaction dataset. The correct recognition rate of the proposed algorithm is 85%, which indicates that the proposed algorithm is simple and effective than others. © 2021 Inderscience Enterprises Ltd.

关键词： K-means clustering

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Flame detection via quaternion discrete cosine transform based spectral saliency

International Journal of Control and Automation

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International Journal of Control and Automation 2016年第9期9卷 343-352页

作者： Zhang, Haiyan Gao, Shangbing Xue, Jianxun Zheng, Hao Faculty of computer and software Jiangsu Provincial Key Laboratory for advanced manufacturing technology Huaiyin Institute of Technology Huai'an China The Key Laboratory for traffic and transportation security of Jiangsu Province Huai'an China Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing China

To address the problem of detecting the specific object of flame, this paper proposes quaternion discrete cosine transform based video flame detection algorithm in conjunction with the bottom-up visual attention mechanisms and the flame features. According to the characteristics of the flame to be detected, the brightness, color, texture and motion information of the image are respectively defined and extracted to form a quaternion image, and then the flame saliency map is produced by Quaternion discrete cosine Transform with this quaternion image. Extensive experimental results verify the accuracy and robustness of the proposed method on flame detection. © 2016 SERSC.

关键词： Discrete cosine transforms

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Truncated Nuclear Norm Based Low Rank Embedding 12th

Truncated Nuclear Norm Based Low Rank Embedding

引用

12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Zhang, Fanlong Chang, Heyou Yang, Guowei Yang, Zhangjing Wan, Minghua School of Technology Nanjing Audit University Nanjing211815 China Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing211171 China

ISBN: (纸本)9783319699226

Dimensionality reduction, also called feature extraction, is an important issue in pattern recognition. However, many existing dimensionality reduction methods, such as principal component analysis, fail when there exist noises in data, especially for noise caused by outliers or corruption. Recently, a robust method, named low-rank embedding (LRE) is proposed, which uses the nuclear norm for characterizing the low rank structure hided in the data. However, one major limitation of the nuclear norm is that each singular value is treated equally, since the nuclear norm is defined as the sum of all singular values. Thus the rank function may not be well approximated in practice. To overcome this drawback, this paper presents a truncated nuclear norm based low rank embedding (Truncated-LRE). The truncated nuclear norm can approximate the rank function more accurately than nuclear norm. Experimental results show encouraging results of the proposed methods in comparison with the state-of-the-art matrix dimensionality reduction methods. © 2017, Springer International Publishing AG.

关键词： Feature extraction

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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China School of State Key Laboratory of Novel Software Technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision ***,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this ***,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision *** the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping ***,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision *** decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into ***,the decision variable with the strongest interaction is added to each *** minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different *** was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our *** with the other algorithms,our method is still at an advantage.

关键词： Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

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Robust Matrix Discriminative analysis for Feature Extraction From Hyperspectral Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第5期10卷 2002-2011页

作者： Hang, Renlong Liu, Qingshan Sun, Yubao Yuan, Xiaotong Pei, Hucheng Plaza, Javier Plaza, Antonio Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China Beijing Electro-Mechanical Engineering Institute Beijing China Hyperspectral Computing Laboratory University of Extremadura Caceres Spain

Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibration errors, and other issues, HSIs can be noisy. These corrupted data easily degrade the performance of the MDA. In this paper, a robust MDA (RMDA) model is proposed to address this important issue. Specifically, based on the prior knowledge that the pixels in a small spatial neighborhood of the HSI lie in a low-rank subspace, a denoising model is first employed to recover the intrinsic components from the noisy HSI. Then, the MDA model is used to extract discriminative spatial-spectral features from the recovered components. Besides, different HSIs exhibit different spatial contextual structures, and even a single HSI may contain both large and small homogeneous regions simultaneously. To sufficiently describe these multiscale spatial structures, a multiscale RMDA model is further proposed. Experiments have been conducted using three widely used HSIs, and the obtained results show that the proposed method allows for a significant improvement in the classification performance when compared to other LDA-based methods.

关键词： Hyperspectral image (HSI) classification multiscale fusion robust matrix discriminative analysis (RMDA) spatial-spectral feature extraction (FE)

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Plausible Heterogeneous Graph k-Anonymization for Social Networks

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Tsinghua Science and Technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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Recognition of Natural Road Sign Based on the Improved Curvature Feature

Recognition of Natural Road Sign Based on the Improved Curva...

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The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Yanqing Wang Hao Zheng Weiwei Chen Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing Xiaozhuang University

To solve the recognition of road sign with an intelligent vehicle in vision-based navigation, road sign extraction and matching techniques required in outdoor scene was proposed in this paper. The method of the improved curvature based on feature extraction and binary description took the advantage of reasonable features distribution to overcome the problems of traditional features uneven distribution. Binary description method was represented to solve the real-time problem of feature matching. Through the validity and real-time performance of different algorithms are compared by experiments and indicate that the method can not only overcome negative influences from the disturb of non-targets, while spending on average only 46 ms processing each frame, but also meet the requirements of robustness, real-time, and accuracy.

关键词： Feature matching Feature extraction Road sign Curvature algorithm

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Research on Target Extraction Technology of Fruit and Vegetable Images in the Complex Environment

Research on Target Extraction Technology of Fruit and Vegeta...

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The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Yanqing Wang Hao Zheng Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing Xiaozhuang University

Considering the difficulty of fruit and vegetable images with uneven illumination and uncontrolled backgrounds, this paper proposed an image preprocess algorithm based on visual subject detection. Firstly, we can use manifold ranking to significance test and to acquire significance images, then use gradient images and position weighting to get cutting images, finally adjust the image brightness and remove image noise to complete the image *** experiment results demonstrated the robustness and real-time of the proposed algorithm which can segment images accurately and the accuracy is higher than 91%.

关键词： Manifold ranking Significance images Gradient images Image segment

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