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International Journal of Multimedia and Ubiquitous Engineering 2016年第7期11卷 103-118页

作者： Xu, Zhuoming Guo, Wenping Wu, Ting Ni, Lixian College of Computer and Information Hohai University Nanjing210098 China Institute of Image Processing & Pattern Recognition Taizhou University Taizhou317000 China

The W3C Open Annotation Community Group’s Open Annotation (OA) Data Model is emerging as a standardized Web Annotation Data Model. We argue that a friendly interactive visual user interface for OA Data Model based annotation tools, which is largely based on an effective user interface model, plays an essential role in the usability of the tools. In this paper we propose an Interactive Visual User Interface Model for Open Annotations (or simply IVUIM4OA), which allows the annotating user to dynamically transform OA data into a visual representation and to interactively manipulate the visual objects to modify the OA data or produce new one. IVUIM4OA is an adaptation of a generic information visualization model, the Data State Reference Model, for the specifics of the visualization of OA data;it is further extended with view operators that enable the user to interact with the visual objects. We implemented a prototype multimedia annotation tool on the basis of IVUIM4OA and conducted case study experiments with the prototype tool. The implementation and experimental results show that the proposed IVUIM4OA is feasible and works well with the OA Data Model, in such a way that the annotating user can obtain a better understanding of and more intuitive interaction with the annotation data. © 2016 SERSC.

关键词： Human computer interaction

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A FAST ADAPTIVE SPATIO-TEMPORAL 3D FEATURE FOR VIDEO-BASED PERSON RE-IDENTIFICATION

A FAST ADAPTIVE SPATIO-TEMPORAL 3D FEATURE FOR VIDEO-BASED P...

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IEEE International Conference on image processing

作者： Zheng Liu Jiaxin Chen Yunhong Wang Laboratory of Intelligent Recognition and Image Processing School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467399623

Video-based person re-identification has become a hot topic in the field of research on computer vision and intelligent surveillance, which is more robust to the variations in a person's appearance than single-shot based methods and involves space-time information. However, the most existing spatiotemporal features have been proposed for action recognition that they mainly focus on the exact spatial changes over time. Unlike action recognition, pedestrians captured in person re-identification problem show similar and cyclic walking activities. The essential spatio-temporal information for person re-identification is the statistical information over time. In this paper, we propose a novel spatio-temporal feature, namely Fast Adaptive Spatio-Temporal 3D feature (FAST3D), for video-based person re-identification. The feature is able to extract the statistical motion information based on densely computed multi-direction gradients and an adaptive fusion process. We evaluate our method on two challenging datasets and the experimental results show the effectiveness and efficiency of the proposed feature.

关键词： Person re-identification Spatio-temporal feature Efficient computation Relevance metric learning

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Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

arXiv

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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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Pulsar Candidate Selection by Assembling Positive Sample Emphasized Classifiers

Pulsar Candidate Selection by Assembling Positive Sample Emp...

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International Conference on Computational Intelligence and Security

作者： Yao Yao Xin Xin Ping Guo School of Computer Science and Technology Beijing Institute of Technology Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781509048410

Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminate radio frequency interference or other noises. Recently, machine learning methods are adopted to automate this task, which saves human labor and makes it possible for processing millions of observations efficiently. Considering the number of positive training samples are relatively too small and the cost of incorrectly labeling a real pulsar candidate as negative is large, we propose a novel hierarchical candidate-sifting model by emphasizing the cost of incorrect prediction of positive samples and assembling multiple classifiers trained with different weighting parameters. Experiments on three pulsar selection datasets demonstrate our proposed method improves the pulsar-sifting performance a lot according to several standard evaluation metrics.

关键词： Training Support vector machines Predictive models Feature extraction Measurement Labeling Histograms

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Multi-sensor federated unscented Kalman filtering algorithm in intermittent observations

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High Technology Letters 2015年第2期21卷 132-139页

作者：胡振涛 Hu Yumei Li Song Institute of Image Processing and Pattern Recognition Henan University College of Computer and Information Engineering Henan University

Aiming at the adverse effect caused by limited detecting probability of sensors on filtering preci- sion of a nonlinear system state, a novel muhi-sensor federated unscented Kalman filtering algorithm is proposed. Firstly, combined with the residual detection strategy, effective observations are cor- rectly identified. Secondly, according to the missing characteristic of observations and the structural feature of unscented Kalman filter, the iterative process of the single-sensor unscented Kalman filter in intermittent observations is given. The key idea is that the state estimation and its error covariance matrix are replaced by the state one-step prediction and its error covariance matrix, when the phe- nomenon of observations missing occurs. Finally, based on the realization mechanism of federated filter, a new fusion framework of state estimation from each local node is designed. And the filtering precision of system state is improved further by the effective management of observations missing and the rational utilization of redundancy and complementary information among multi-sensor observa- tions. The theory analysis and simulation results show the feasibility and effectiveness of the pro- posed algorithm.

关键词： nonlinear estimation intermittent observations unscented Kalman filter federated filter

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Unsupervised Feature Selection Algorithm Based on Sparse Representation

Unsupervised Feature Selection Algorithm Based on Sparse Rep...

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International Conference on Systems and Informatics

作者： Guoqing Cui Jie Yang Masoumeh Zareapoor Jiechen Wang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Complex Network and Control Lab Shanghai Jiao Tong University

ISBN: (纸本)9781509055227

Feature selection, as a preprocessing step to machine learning, plays a pivotal role in removing irrelevant data, reducing dimensionality and improving performance evaluations. Recent years, sparse representation has become a useful tool for both supervised and unsupervised feature selection. So far, most of these algorithms still have many problems such as large computation load, performance with poor stability. Thus, this paper proposes a new unsupervised feature selection algorithm via sparse representation (UFSSR), with respect to efficiency and effectiveness. Firstly, this paper reconstructs part of data matrix via sparse representation, which makes the proposed algorithm be robust and independent of domain knowledge. Then, to reduce the reconstruction error, a new feature evaluation function is given to rank all features. Theoretical analysis and experiments compared with many popular algorithms on a set of datasets demonstrate the improvements brought by UFSSR.

关键词： Unsupervised feature selection Sparse representation Feature evaluation function Computation load Stability

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MRI denoising using randomized version of nonlocal means method

MRI denoising using randomized version of nonlocal means met...

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2016 International Conference on image processing, Computer Vision, and pattern recognition, IPCV 2016

作者： Hu, Jinrong He, Jia Fu, Ying Wu, Xi Zhou, Jiliu School of Computer and Soft Engineering Xihua University Chengdu610039 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Chengdu University Chengdu610106 China School of Computer Science Chengdu University of Information Technology Chengdu610225 China

ISBN: (纸本)1601324421

- Non-local mean (NLM) algorithm has been implemented effectively in MRI denoising and is always limited by its computational complexity. To reduce the computational burden of NLM in 3D MRI dataset, in this paper, we used a randomized version of NLM algorithm to remove the noise in MRI data. The random NLM algorithm seeds up the classical NLM by computing a small subset of image patch distances, which are randomly select according to the uniform sampling pattern. Numerical experiments demonstrate that the random NLM can achieve a competitive denoising result at a low sampling rate (0.05) in 3D MRI dataset while reducing the runtime dramatically. © CSREA Press.

关键词： Magnetic resonance imaging

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Robust visual tracking via inverse nonnegative matrix factorization

Robust visual tracking via inverse nonnegative matrix factor...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Fanghui Liu Tao Zhou Keren Fu Irene Y. H. Gu Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden

The establishment of robust target appearance model over time is an overriding concern in visual tracking. In this paper, we propose an inverse nonnegative matrix factorization (NMF) method for robust appearance modeling. Rather than using a linear combination of nonnegative basis vectors for each target image patch in conventional NMF, the proposed method is a reverse thought to conventional NMF tracker. It utilizes both the foreground and background information, and imposes a local coordinate constraint, where the basis matrix is sparse matrix from the linear combination of candidates with corresponding nonnegative coefficient vectors. Inverse NMF is used as a feature encoder, where the resulting coefficient vectors are fed into a SVM classifier for separating the target from the background. The proposed method is tested on several videos and compared with seven state-of-the-art methods. Our results have provided further support to the effectiveness and robustness of the proposed method.

关键词： Target tracking Encoding Visualization Robustness Computational modeling Sparse matrices Support vector machines

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LEARNING FULL-RANGE AFFINITY FOR DIFFUSION-BASED SALIENCY DETECTION

LEARNING FULL-RANGE AFFINITY FOR DIFFUSION-BASED SALIENCY DE...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Keren Fu Irene Y.H. Gu Jie Yang Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781479999897

In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range affinity between two arbitrary graph nodes. The method differs from the previous existing work where implicit diffusion was formulated as a ranking problem on a graph. In the proposed method, the affinity learning is achieved in a unified graph-based semi-supervised manner, whose outcome is leveraged for global propagation. By properly selecting an affinity learning model, the proposed ALD outperforms the ranking-based diffusion in terms of accurately detecting salient objects and enhancing the correct salient objects under a range of background scenarios. By utilizing the ALD, we propose an enhanced saliency detector that outperfomis 7 recent state-of-the-art saliency models on 3 benchmark datasets.

关键词： Saliency detection graph-based diffusion affinity learning semi-supervised learning Semi-supervised Learning affinity diffusion Adrenoleukodystrophy Affine transformation Object detection Learning

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Multi-level thresholding for pupil location in eye-gaze tracking systerm

Multi-level thresholding for pupil location in eye-gaze trac...

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Chen, Mo-Han Wen, Jing Zhu, Yu Xing, Hao-Yang Wang, Yi College of Computer Science Chongqing University Chongqing China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University China Magnetic Resonance Imaging Research Centre Huaxi Hospital Sichuan Chengdu China

ISBN: (纸本)9781509003891

A new pupil location methodis proposed in eye-gaze tracking system. Firstly, input images are enhanced in order to reduce the influence of illumination. Secondly, multiple candidate thresholds are obtained in terms of the valleys in histogram, and then different segmentation results are available. Further, a fusion method based on the overlaps of segmentation results is proposed which acquired candidate regions and the eye region is obtained according to the entropy information of candidate regions. Thirdly, a simple and effective threshold segmentation method based on eye characteristic is employed and pupil area is obtained. Finally, the center of pupil is acquired by ellipse fitting. Experimental results demonstrated that the proposed methods were effective to improve pupil location accuracy. © 2016 IEEE.

关键词： Entropy

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