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IEEE International Geoscience and Remote Sensing Symposium

作者： Shaoteng Liu Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning Northwestern Polytechnical University Xi'an Shaanxi P. R. China Unmanned System Research Institute Northwestern Polytechnical University Xi'an Shaanxi P. R. China University of Chinese Academy of Sciences Beijing P. R. China

Scene classification of very high resolution remote sensing images is becoming more and more important because of its wide range of applications. However, previous works are mainly based on handcrafted features which do not have enough adaptability and expression ability. In this paper, inspired by the attention mechanism of human visual system, we propose a novel attention based network (AttNet) for scene classification. It can focus selectively on some key areas of images so that it can abandon redundant information. Essentially, AttNet gives a way to readjust the signal of supervision, and it is one of the first successful attempts on visual attention for remote sensing scene classification. Our method is evaluated on the UC Merced Land-Use Dataset, in comparison with some state-of-the-art methods. The experimental result shows that the proposed method makes a great improvement on both convergence speed and classification accuracy, and it also shows the effectiveness of visual attention for this task.

关键词： Remote sensing Feature extraction Visualization Training Image resolution Convergence

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Largest center-specific margin for dimension reduction

Largest center-specific margin for dimension reduction

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Jian'an Zhang Yuan Yuan Feiping Nie Qi Wang School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi'an 710072 Shaanxi China

ISBN: (纸本)9781509041183

Dimensionality reduction plays an important role in solving the "curse of the dimensionality" and attracts a number of researchers in the past decades. In this paper, we proposed a new supervised linear dimensionality reduction method named largest center-specific margin (LCM) based on the intuition that after linear transformation, the distances between the points and their corresponding class centers should be small enough, and at the same time the distances between different unknown class centers should be as large as possible. On the basis of this observation, we take the unknown class centers into consideration for the first time and construct an optimization function to formulate this problem. In addition, we creatively transform the optimization objective function into a matrix function and solve the problem analytically. Finally, experiment results on three real datasets show the competitive performance of our algorithm.

关键词： Dimensionality Reduction LCM center-specific Method dimensionality reduction Lowest common multiple matrix function Objective function Curse of Dimensionality Tumor margin status Intuition

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Semi-supervised classification via both label and side information

Semi-supervised classification via both label and side infor...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Rui Zhang Feiping Nie Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi'an 710072 Shaanxi China

ISBN: (纸本)9781509041183

As for the semi-supervised learning, both label and side information serve as pretty significant indicators for the classification. However, majority of the associated works only focus on one side of the road. In other words, either the label information or the side information is utilized instead of taking both of them into consideration simultaneously. To address the referred defect, we propose a graph-based semi-supervised learning (GSL) problem via building the intrinsic graph and the penalty graph upon both label and side information. To efficiently unravel the proposed GSL problem, a novel quadratic trace ratio (QTR) method is proposed based on solving the associated QTR problem, which is the equivalent counterpart of the GSL problem. Besides, a parameter-free similarity is further derived and utilized. Consequently, a novel semi-supervised classification (SC) algorithm can be summarized by virtue of the proposed GSL problem and QTR method.

关键词： soft label side information graph-based semi-supervised learning quadratic trace ratio problem

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Embedding structured contour and location prior in siamesed fully convolutional networks for road detection

Embedding structured contour and location prior in siamesed ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Junyu Gao Qi Wang Yuan Yuan authors are with School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi'an Shaanxi PR China

Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving. Recently, many deep learning methods spring up for this task because they can extract high-level local features to find road regions from raw RGB data, such as Convolutional Neural Networks (CNN) and Fully Convolutional Networks (FCN). However, how to detect the boundary of road accurately is still an intractable problem. In this paper, we propose a siamesed fully convolutional network (named as “s-FCN-loc”) based on VGG-net architecture, which is able to consider RGB-channel, semantic contour and location prior simultaneously to segment road region elaborately. To be specific, the s-FCN-loc has two streams to process original RGB images and contour maps respectively. At the same time, the location prior is directly appended to the last feature map to promote the final detection performance. Experiments demonstrate that the proposed s-FCN-loc can learn more discriminative features of road boundaries and converge 30% faster than the original FCN during the training stage. Finally, the proposed approach is evaluated on KITTI road detection benchmark, and achieves a competitive result.

关键词： Roads Semantics Feature extraction Streaming media Image segmentation Training Image edge detection

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Projected Clustering via Robust Orthogonal Least Square Regression with Optimal Scaling

Projected Clustering via Robust Orthogonal Least Square Regr...

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International Joint Conference on Neural Networks

作者： Rui Zhang Feiping Nie Xuelong Li School of Computer Science and Center for Optical Imagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Center for Optical Imagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences

ISBN: (纸本)9781509061839

The orthogonal least square regression (OLSR) serves as a pretty significant problem for the dimensionality reduction. Due to lack of the scale change in OLSR, the scaling term is at first introduced to OLSR to build up a novel orthogonal least square regression with optimal scaling (OLSR-OS) problem. However, OLSR-OS is still sensitive to the outliers, such that associated results could be fallacious. To strengthen the robustness of OLSR-OS, we propose an original robust OLSR-OS (ROLSR-OS) problem in ?2,1 -norm. To tackle a more ill-defined situation, ROLSR-OS in ?_2,1-norm can be further extended to ROLSR-OS in capped ?2 -norm. Besides, the associated ROLSR-OS methods could be derived by solving the re-weighted counterparts of ROLSR-OS problems in both norms. Moreover, the equivalence between the re-weighted counterparts and the original ROLSR-OS problems is also provided along with the convergence analysis of the proposed ROLSR-OS methods. Accordingly, both the optimal scaling and weight can be achieved automatically via the proposed ROLSR-OS approaches. Specifically, the proposed ROLSR-OS methods are self-adaptive, such that the smaller weight would be automatically assigned to the term with larger outliers to enhance the robustness. Consequently, projected clustering and modified projected clustering under the proposed ROLSR-OS problems are further investigated both theoretically and experimentally.

关键词： Least squares method Scale formation Cluster analysis outliers Orthogonal AUTOMATIC dimensionality reduction Dental Scaling Scaling Regression (Psychology) Robustness Optimal

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ROBUST ADAPTIVE SPARSE learning METHOD FOR GRAPH CLUSTERING

ROBUST ADAPTIVE SPARSE LEARNING METHOD FOR GRAPH CLUSTERING

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IEEE International Conference on Image Processing

作者： Mulin Chen Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi'an Shaanxi P. R. China Unmanned System Research Institute (USRI) Northwestern Polytechnical University Xi'an Shaanxi P. R. China University of Chinese Academy of Sciences Beijing P. R. China

Graph clustering aims to group the data into clusters according to a similarity graph, and has received sufficient attention in computer vision. As the basis of clustering, the quality of graph affects the results directly. In this paper, a Robust Adaptive Sparse learning (RASL) method is proposed to improve the graph quality. The contributions made in this paper are three fold: (1) the sparse representation technique is employed to enforce the graph sparsity, and the l_(2,1) norm is introduced to improve the robustness;(2) the intrinsic manifold structure is captured by investigating the local relationship of data points;(3) an efficient optimization algorithm is designed to solve the proposed problem. Experimental results on various real-world benchmark datasets demonstrate the promising results of the proposed graph-based clustering method.

关键词： Robustness Manifolds Linear programming Clustering algorithms Sparse matrices Optimization Toy manufacturing industry

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Anchor-based group detection in crowd scenes

Anchor-based group detection in crowd scenes

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Mulin Chen Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi'an 710072 Shaanxi China Center for OPTical IMagery Analysis and Learning (OPTIMAL) Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences 710119 Shaanxi China

ISBN: (纸本)9781509041183

Group detection aims to classify pedestrians into categories according to their motion dynamics. It's fundamental for analyzing crowd behaviors and involves a wide range of applications. In this paper, we propose a Anchor-based Manifold Ranking (AMR) method to detect groups in crowd scenes. Our main contributions are threefold: (1) the topological relationship of individuals are effectively investigated with a manifold ranking method;(2) global consistency in crowds are accurately recognized by a coherent merging strategy;(3) the number of groups is decided automatically based on the similarity graph of individuals. Experimental results show that the proposed framework is competitive against the state-of-the-art methods.

关键词： Crowd Motion Group Detection Manifold Structure Clustering

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OPTIMAL NEIGHBORING RECONSTRUCTION FOR HYPERSPECTRAL BAND SELECTION

OPTIMAL NEIGHBORING RECONSTRUCTION FOR HYPERSPECTRAL BAND SE...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Fahong Zhang Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning Northwestern Polytechnical University Xi'an Shaanxi P. R. China Unmanned System Research Institute Northwestern Polytechnical University Xi'an Shaanxi P. R. China Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an Shaanxi P. R. China

Band selection, as an effective and popular dimensional reduction methods for hyperspectral image (HSI), has raised wide attention in recent years. In this paper, we propose a novel band selection method called optimal neighboring reconstruction (ONR). Compared to conventional methods, ONR mainly has following advantages. 1) It is globally optimal, which means the best combination of bands towards the designed objective function can be achieved. 2) It sufficiently exploits the neighboring structure among bands, so can effectively reduce the redundancy while maintaining the discrimination among bands. Experiments on three real data sets show that the proposed method has excellent performance.

关键词： Linear programming Hyperspectral imaging Noise measurement Support vector machines Correlation Dynamic programming Image reconstruction

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ROBUST PCANET FOR HYPERSPECTRAL IMAGE CHANGE DETECTION

ROBUST PCANET FOR HYPERSPECTRAL IMAGE CHANGE DETECTION

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IEEE International Geoscience and Remote Sensing Symposium

作者： Zhenghang Yuan Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning Northwestern Polytechnical University Xi'an Shaanxi P. R. China Unmanned System Research Institute Northwestern Polytechnical University Xi'an Shaanxi P. R. China Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xian Shaanxi P. R. China

Deep learning is an effective tool for handling high-dimensional data and modeling nonlinearity, which can tackle the hyperspectral data well. Usually deep learning methods need a large number of training samples. However, there is no labeled data for training in change detection (CD). Considering these, this paper develops an unsupervised Robust PCA network (RPCANet) for hyperspectral image CD task. The main contributions of this work are twofold: 1) An unsupervised convolutional neural networks named RPCANet is proposed to handle the hyperspectral image CD;2) An effective CD framework using the RPCANet and change vector analysis (CVA) is designed to achieve better CD performance with more powerful features. Experimental results on real hyperspectral data sets demonstrate the effectiveness of the proposed method.

关键词： Hyperspectral image Change detection (CD) Robust PCA network (RPCANet) Change vector analysis (CVA)

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Robust Sparse Coding for Mobile Image Labeling on the Cloud

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IEEE Transactions on Circuits and Systems for Video Technology 2017年第1期27卷 62-72页

作者： Tao, Dapeng Cheng, Jun Gao, Xinbo Li, Xuelong Deng, Cheng School of Information Science and Engineering Yunnan University Kunming650091 China Laboratory for Human Machine Control Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Chinese University of Hong Kong Hong Kong School of Electronic Engineering Xidian University Xi'an710071 China State Key Laboratory of Transient Optics and Photonics Center for OPTical IMagery Analysis and Learning Xi'an Institute of Optics and Precision Mechanics Xi'an710119 China

With the rapid development of the mobile service and online social networking service, a large number of mobile images are generated and shared on the social networks every day. The visual content of these images contains rich knowledge for many uses, such as social categorization and recommendation. Mobile image labeling has, therefore, been proposed to understand the visual content and received intensive attention in recent years. In this paper, we present a novel mobile image labeling scheme on the cloud, in which mobile images are first and efficiently transmitted to the cloud by Hamming compressed sensing, such that the heavy computation for image understanding is transferred to the cloud for quick response to the queries of the users. On the cloud, we design a sparse correntropy framework for robustly learning the semantic content of mobile images, based on which the relevant tags are assigned to the query images. The proposed framework (called maximum correntropy-based mobile image labeling) is very insensitive to the noise and the outliers, and is optimized by a half-quadratic optimization technique. We theoretically show that our image labeling approach is more robust than the squared loss, absolute loss, Cauchy loss, and many other robust loss function-based sparse coding methods. To further understand the proposed algorithm, we also derive its robustness and generalization error bounds. Finally, we conduct experiments on the PASCAL VOC'07 data set and empirically demonstrate the effectiveness of the proposed robust sparse coding method for mobile image labeling. © 1991-2012 IEEE.

关键词： Social networking (online)

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