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19th IEEE International Conference on Data Mining Workshops, ICDMW 2019

作者： Shang, Fanhua Zhang, Zhihui An, Yuying Hu, Yang Liu, Hongying Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China

ISBN: (纸本)9781728146034

In recent years, the stochastic variance reduced gradient (SVRG) method has been used for singular vector decomposition (SVD), called VR-SVD, and VR-SVD only uses simple variance reduction iterations, yet can obtain an exponential convergence rate to the optimal solution. Considering that recent years have witnessed great application prospects in large-scale SVD, this paper proposes two efficient asynchronous parallel stochastic variance reduction algorithms. Moreover we analyze their convergence properties in theory, and both dense and sparse asynchronous parallel variants can converge with the rate of O(1/T). Finally, we extend our algorithms for the SVD problems with multiple singular vectors. Our extensive experimental results show that our algorithms attain ideal parallel speedup ratios and achieve the same PSNR values in a shorter time, and thus they can be widely used in practice. © 2019 IEEE.

关键词： Big data

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Directional Filter for SAR images Based on Nonsubsampled Contourlet Transform and Immune Clonal Selection

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International Journal of Automation and computing 2009年第3期6卷 245-253页

作者： Xiao-Hui Yang Li-Cheng Jiao Deng-Feng Li Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi＇an 710071 PRC Institute of Applied Mathematics School of Mathematics and Information Sciences Henan University Kaifeng 475004 PRC

A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.

关键词： Directional filter nonsubsampled contourlet transform （NSCT） immune clonal selection optimization （ICSO） syntheticaperture radar （SAR）.

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Weakly Supervised Land Cover Classification Method for Large-Scale Multi-Resolution Labeled Satellite images Data Sets

Weakly Supervised Land Cover Classification Method for Large...

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2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020

作者： Yin, Shuting Chen, Dafan Ma, Chengconghui Lian, Yanchao Jiao, Licheng Liu, Fang Joint International Research Laboratory of Intelligent Perception and Computation School of Artificial Intelligence Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Shaanxi Province Xian710071 China

The global land cover map obtained by satellite is of vital importance to record land information. Despite the large amount of data acquired, rare annotations are available. Weakly-supervised learning methods can help to get use of extensive existing data resources and reduce the cost of manual labeling, which is of great significance for large-scale land cover mapping. Considering that the low-resolution labels are inaccurate and inexact and that high-resolution labels are rare, which is likely to cause training overfitting, we propose a method that can effectively apply multi-resolution labels, combining machine learning and deep learning. First, high-resolution labels of the validation set are used to train DeepLabv3+ (DLv3) and Random Forest (RF), which are applied to predict the training set then. The intersection of the prediction results is then used to supervise the training of DLv3 as pseudo-labels together with the high-resolution labels. We also added strategies such as model fusion, data augmentation and weighted training to improve classification accuracy. In the end, our method achieves the average accuracy (AA) of 0.609, ranking 3rd in Track 2 of the IEEE Data Fusion Contest (DFC) of 2020. © 2020 IEEE.

关键词： Decision trees

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Augmentation Based Synthetic Sampling and Ensemble Techniques for Imbalanced Data Classification 5th

Augmentation Based Synthetic Sampling and Ensemble Techniqu...

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5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Asefaw, Wakjira Mulugeta Shang, Ronghua Okoth, Michael Aggrey Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Shaanxi Xi’an710071 China

ISBN: (纸本)9783031149023

The imbalance data problem appears in data mining fields and has recently attracted the attention of researchers. In order to solve this problem, scholars proposed various approaches such as undersampling majority class, oversampling minority class, synthetic Minority Oversampling (SMOTE) technique, Proximity Weighted Random Affine Shadowsampling (ProWRAS), etc. However, this work proposes a new method called Augmentation Based Synthetic Sampling (ABS) for imbalanced data classification that concatenates data to predict features with imbalance problems. The proposed study integrates sampling and concatenated features to generate synthetic data. This study shows the ability of the proposed method and the average of the AUC (area under the curve) to generate good data samples while experimenting compared to the previous study. In addition, this study merged the proposed method with the boosting to create a technique known as ABSBoost. Therefore, the experimental outcomes show that the proposed ABS method and ABSBoost are effective on the given datasets. © 2022, IFIP International Federation for Information Processing.

关键词： Data mining

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基于第二代Bandelet域隐马尔可夫树模型的图像分割

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自动化学报 2009年第5期35卷 498-504页

作者：侯彪徐婧刘凤焦李成 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

第二代Bandelet可以充分利用图像的内在几何正则性特点,并能自适应获得图像的最优表示.本文采用隐马尔可夫树(Hidden Markov tree,HMT)模型对图像的第二代Bandelet系数建模,通过多尺度参数训练和基于上下文的最大后验概率进行图像分割.... 详细信息

第二代Bandelet可以充分利用图像的内在几何正则性特点,并能自适应获得图像的最优表示.本文采用隐马尔可夫树(Hidden Markov tree,HMT)模型对图像的第二代Bandelet系数建模,通过多尺度参数训练和基于上下文的最大后验概率进行图像分割.为了评价本文方法的性能,我们分别选择合成纹理图像、航拍图像和SAR图像进行实验,并与小波域HMT模型分割方法(WD-HMTseg)和Contourlet域HMT模型分割方法(CHM Tseg)进行比较说明算法的有效性.实验结果表明本文方法不但在边缘准确性和区域一致性上有明显改进,而且也降低了纹理图的错分概率.

关键词：图像分割第二代Bandelet 隐马尔可夫树模型小波 Contourlet变换

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Change Detection in SAR images Based on Evolutionary Multiobjective Optimization and Superpixel Segmentation 11th

Change Detection in SAR Images Based on Evolutionary Multiob...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Gao, Tianqi Qiao, Wenyuan Li, Hao Gong, Maoguo Li, Gengchao Min, Jie School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi’an710071 China

ISBN: (纸本)9783030720612

Synthetic aperture radar (SAR) imagery has been widely used in the field of remote sensing image change detection. However, its disadvantage of strong coherent multiplicative noise reduces the accuracy of change detection results. This paper proposes a novel SAR image change detection method, which is mainly comprised of three steps. Firstly, the difference image (DI) which is generated by log-ratio operator is segmented into superpixels by Simple Linear Iterative Clustering (SLIC) Algorithm. Secondly, superpixels are encoded uniformly in order to be utilized as the training samples, and deep neural network is used to extract deep features of DI. Finally, this paper designs an improved clustering algorithm which is optimized by Non-dominated Sorting Genetic Algorithm (NSGA-II). When the deep features of DI are used to cluster, Bhattacharyya distance between two categories of samples is selected as the similarity measurement. Taking the logarithmic likelihood function of clustering algorithm and the Bhattacharyya distance between the two categories as two optimization objectives, NSGA-II algorithm is used to optimize the model, and a set of pareto optimal solutions are thus generated. Compared with various indexes for accuracy evaluation, the map which has the highest accuracy is the final change detection map. Experimental results on real synthetic aperture radar datasets show that the proposed method is superior to other classical change detection methods, which demonstrates its effectiveness, feasibility, and superiority of the proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Clustering algorithms

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Community detection in dynamic social networks based on multiobjective immune algorithm

Community detection in dynamic social networks based on mult...

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作者： Gong, Mao-Guo Zhang, Ling-Jun Ma, Jing-Jing Jiao, Li-Cheng Key Lab of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an 710071 China

Community structure is one of the most important properties in social networks, and community detection has received an enormous amount of attention in recent years. In dynamic networks, the communities may evolve over time so that pose more challenging tasks than in static ones. Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms. In this paper, a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks. It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method. Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms. © 2012 Springer Science+Business Media, LLC & Science Press, China.

关键词： Multiobjective optimization

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A memetic kernel clustering algorithm for change detection in SAR images

A memetic kernel clustering algorithm for change detection i...

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Li, Yangyang Lu, Gao Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation Xidian University XianShaanxi710071 China

ISBN: (纸本)9789811036132

For SAR images change detection problem, a memetic kernel clustering algorithm (MKCA) is proposed. SAR image change detection problem as an optimization problem can solved by kernel clustering method. In this paper, the kernel function can transform nonlinear data into a high dimensional feature space, and increases the probability of the linear separability of the patterns within the transformed space and simplifies the associated data structure. Meanwhile local learning operators are designed to further enhance the ability of global exploration and promote performance of classification. Finally, to evaluate this method, the performance of the proposed method has been compared with some classical clustering algorithms for change detection in SAR (synthetic aperture radar) images. The experiments showed that the proposed algorithm can reduce the number of missed alarms and false alarms. So the proposed algorithm can effectively separate the unchanged area and the changed area. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Clustering algorithms

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Building a Stereo and Wide-View Hybrid RGB/FIR Imaging System for Autonomous Vehicle

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IEEE Sensors Journal 2022年第2期22卷 1638-1651页

作者： Zhang, Yigong Sun, Zezhou Wu, Banghe Yang, Jian Kong, Hui Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China University of Macau Taipa China

In this work, a novel paradigm is proposed to build a hybrid RGB/FIR camera, which outputs pixel-wisely aligned and temporally synchronized RGB and FIR frames simultaneously. Compared with the existing schemes, we innovate in several ways to make it compact, practical, and extendable for real-world applications. First, we propose a new structure layout design of the hybrid camera, and we also design a removable adjuster to detach the hybrid camera from the base platform. For RGB and FIR image registration, we have achieved a pixel-wise spatial registration of the FIR and RGB frames by a two-step alignment, where we propose a ghost-effect removal scheme to remove the ghost effect from the rolling-shutter-based FIR camera. Finally, multiple hybrid cameras are extended to a hybrid camera array, through which we can obtain wide-view spatially aligned FIR, RGB and disparity images simultaneously. The experimental results demonstrate the accuracy of the hybrid camera and the effectiveness of the designed stereo wide-view hybrid camera system. © 2001-2012 IEEE.

关键词： Cameras

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Quantum-behaved particle swarm optimization using MapReduce

Quantum-behaved particle swarm optimization using MapReduce

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Li, Yangyang Chen, Zhenghan Wang, Yang Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Joint International Research Laboratory of Intelligent Perception and Computation International Research Center for Intelligent Perception and Computation Xidian University Xi’anShaanxi710071 China

ISBN: (纸本)9789811036132

Quantum-behaved particle swarm optimization (short in QPSO) is an improved version of particle swarm particle (short in PSO), and the performance is superior. But for now, it may not always satisfy the situations. Nowadays, problems become larger and more complex, most serial optimization algorithms cannot deal with the problem or need plenty of computing cost. In this paper, we implement QPSO on MapReduce model, propose MapReduce quantum-behaved particle swarm optimization (short in MRQPSO), and realize QPSO parallel and distributed, which the MapReduce model is a parallel computing programming model. In the experiments, the test results show that MRQPSO is more advanced both on performance of solution and time than QPSO. © Springer Nature Singapore Pte Ltd. 2016.

关键词： MapReduce

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