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Speech communication;10. ITG Symposium

作者： Jalal Taghia Rainer Martin Jalil Taghia Arne Leijon Institute of Communication Acoustics Ruhr-Universitat Bochum Sound and Image Processing Lab KTH Royal Institute of Technology

ISBN: (纸本)9783800734559

Mutual information (MI) is an important information theoretic concept which has many applications in telecommunications, in blind source separation, and in machine learning. More recently, it has been also employed for the instrumental assessment of speech intelligibility where traditionally correlation based measures are used. In this paper, we address the difference between MI and correlation from the viewpoint of discovering dependencies between variables in the context of speech signals. We perform our investigation by considering the linear predictive approximation and the extrapolation of speech signals as examples. We compare a parametric MI estimation approach based on a Gaussian mixture model (GMM) with the knearest neighbor (KNN) approach which is a well-known non-parametric method available to estimate the MI. We show that the GMM-based MI estimator leads to more consistent results.

关键词： Speech Correlation Extrapolation Mutual information Estimation Speech processing Probability distribution

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Top-down saliency by multi-scale contextual pooling

Top-down saliency by multi-scale contextual pooling

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13th Pacific-Rim Conference on Multimedia, PCM 2012

作者： Qiu, Yuanyuan Zhu, Jun Zhang, Rui Huang, Jun Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences China

ISBN: (纸本)9783642347771

Goal-driven top-down mechanism plays an important role in the case of object detection and recognition. In this paper, we propose a top-down computational model for goal-driven saliency detection based on a coding-based classification framework. It consists of four successive steps: feature extraction, descriptor coding, local pooling and saliency prediction. In the step of local pooling, we investigate the effect of multi-scale contextual information for saliency detection and find that there exists an optimal contextual scale to achieve the patch-level feature presentation. On basis of this observation, we propose an approach for automatic scale selection in saliency prediction step. The experimental results demonstrate that our method can effectively improve the performance of goal-driven saliency detection as well as related object detection. © 2012 Springer-Verlag.

关键词： Object detection

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Crowd Event Perception Based on Spatio-temporal Viscous Fluid Field

Crowd Event Perception Based on Spatio-temporal Viscous Flui...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Hang Su Hua Yang Shibao Zheng Yawen Fan Sha Wei Institution of Image Communication and Information Processing Department of EE Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiaotong University Shanghai China

Over the past decades, a wide attention has been paid to crowd control and management in intelligent video surveillance area. In this paper, the authors propose a novel spatiotemporal viscous fluid field to recognize large-scale crowd event with respect to both appearance and driven factor of crowd behavior. Firstly, a spatiotemporal variation matrix is proposed to exploit motion property of a crowd. In particular, the paper exploits characteristics of the matrix with eigenvalue decomposition algorithm and constructs an abstract fluid field to model the crowd motion pattern, which is denoted by spatiotemporal fluid field. Secondly, the paper proposes a spatiotemporal force field to exploit the interaction force between the pedestrians. Furthermore, the fluid and force field constructs a spatiotemporal viscous fluid field. Thirdly, after generating feature with bag of word model, the authors utilize latent Dirichlet allocation model to recognize crowd behavior. The experiments on PETS2009 and UMN datasets show that the proposed method has a better performance for large-scale crowd behavior perception in both robustness and effectiveness comparing with the conventional methods.

关键词： Force Spatiotemporal phenomena Eigenvalues and eigenfunctions Vectors Abstracts Resource management Symmetric matrices

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Robust integrated locally linear embedding

Robust integrated locally linear embedding

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7th Chinese Conference on Biometric Recognition, CCBR 2012

作者： Zhang, Li-Li Xie, Ying Luo, Bin Ding, Chris Tang, Jin Key Lab of Industrial Image Processing & Analysis of Anhui Province Hefei 230039 China School of Computer Science and Technology Anhui University Hefei 230601 China CSE Department University of Texas at Arlington Arlington TX 76019 United States of America

ISBN: (纸本)9783642355059

Many real life applications often bring much high-dimensional and noise-contaminated data from different sources. In this paper, we consider de-noising as well as dimensionality reduction by proposing a novel method named Robust Integrated Locally Linear Embedding. The method combines the two steps in LLE into a single framework and deals with de-noising by solving a l 2,1-l 2 mixed norm based optimization problem. We also derive an efficient algorithm to build the proposed model. Extensive experiments demonstrate that the proposed method is more suitable to exhibit relationship among data points, and has visible improvement in de-noising, embedding and clustering tasks. © 2012 Springer-Verlag.

关键词： Reduction

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An improved full-reference image quality metric based on structure compensation

An improved full-reference image quality metric based on str...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Ke Gu Guangtao Zhai Xiaokang Yang Wenjun Zhang Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processingand Transmissions

During the last two decades, image quality assessment has been a major research area, which considerably helps to promote the development of image processing. Following the tremendous success of Structural SIMilarity (SSIM) index in terms of the correlation between the quality predictions and the subjective scores, many improved algorithms have been further exploited, such as Multi-Scale SSIM (MS-SSIM) and Information content Weighted SSIM (IW-SSIM). However, a growing number of researchers have been devoted to the study of the effects of uneven responses to different image distortion categories on prediction accuracy of the quality metrics. Inspired by this, we propose an improved full-reference image quality assessment paradigm based on structure compensation. Experimental results on laboratory for image and Video Engineering (LIVE) database and Tampere image Database 2008 (TID2008) are provided to confirm our introduced approach has superior prediction performance as compared to mainstream image quality metrics. Besides, it is worth emphasizing that our algorithm not introduces other operators but only applies the SSIM function to compensate itself, and furthermore, it also has an effective capability of image distortion classification.

关键词： Nonlinear distortion Measurement image quality Transform coding Indexes Prediction algorithms

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Denoising Diffusion Tensor images with Shearlet

Denoising Diffusion Tensor Images with Shearlet

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2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： Xiangfen Zhang Bao-Liang Lu Yan Ma Xiaozhong Xu Fangfang Wei Wenjie Xu Institute of Intelligent Computing & Image Processing College of Mechanical and Electronic EngineeringShanghai Normal University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and EngineeringShanghai Jiaotong University MOE-Microsoft Key Lab for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781467321969

Diffusion tensor imaging （DTI） is known to be the best non-invasive imaging modality in providing anatomical information as white-matter fiber bundles. However, the Gaussian noise introduced into the diffusion tensor images can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Gaussian noise, many denoising methods have been presented. In this paper, a shearlet based denosing strategy is introduced. To evaluate the efficiency of the proposed shearlet based denoising method in accounting for the Gaussian noise introduced into the images, the peak to peak signal-to-noise ratio （PSNR）, signal-to-mean squared error ratio （SMSE） and edge keeping index （Beta） metrics are adopted. The experiment results acquired from both the synthetic and real data indicate the good performance of our proposed filter.

关键词： diffusion tensor imaging shearlet transform wavelet transform PSNR SMSE denoising

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Corrigendum to “Graph structure analysis based on complex network” [Digital Signal processing 22 (5) (2012) 713–725]

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Digital Signal processing 2013年第4期23卷 1332-1332页

作者： Jin Tang Bo Jiang Chin-Chen Chang Bin Luo School of Computer Science and Technology Anhui University Hefei 230039 Anhui China Key lab of Image Processing & Analysis of Anhui Province Hefei 230039 Anhui China Department of Information Engineering and Computer Science Feng Chia University Taiwan

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Robust Mobile Spamming Detection via Graph Patterns

Robust Mobile Spamming Detection via Graph Patterns

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International Conference on Pattern Recognition

作者： Yuhang Zhao Zhaoxiang Zhang Yunhong Wang Jianyun Liu Intelligent Recognition and Image Processing Lab Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467322164

Short message service (SMS) is now an indispensable way of social communication. However the mobile spam is getting increasingly serious, troubling users' daily life and ruining the service quality. We propose a novel approach for spam message detection based on mining the underlying social network of SMS activities. Comparing with strategies on keywords or flow detection, our network-based approach is more robust and difficult to defeat by human spammers. Various levels of features are employed to describe multiple aspects of the network, such as static structures, node activities and evolving situations. Experimental results on real dataset illustrate effectiveness of various features, showing our promising results.

关键词： data mining electronic messaging feature extraction mobile computing network theory (graphs) social networking (online) unsolicited e-mail

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A visual comfort metric for stereoscopic 3D video based on SMDE approach

A visual comfort metric for stereoscopic 3D video based on S...

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IEEE International Conference on Signal processing, communications and Computing (ICSPCC)

作者： Bi Ye Jun Zhou Institute of Image Communication and Information Processing Shanghai Jiao Tong University 200240 China Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai China

Visual comfort assessment for stereoscopic video is playing an important role for stereoscopic safety issue. In this paper, we propose a novel visual comfort assessment metric that utilizes interest regions detection approach, which is called Salient Motion Depth Extraction approach in our algorithm. In stereoscopic video shots, salient motion regions where human subjects focus on should have more weights in visual comfort assessment. To achieve better performance, our approach combines salient cues, motion cues with depth cues in order to extract salient motion regions in consideration of depth context. Our visual comfort assessment utilizes local analytical method based on attention model by analyzing disparity features in interest regions extracted by Salient Motion Depth Extraction approach. The experimental results have demonstrated that our proposed visual comfort assessment improves the correlation with the subjective assessment.

关键词： Visualization Context Measurement Feature extraction Humans Stereo image processing Computational modeling

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Pan-sharpening using weighted red-black wavelet

Pan-sharpening using weighted red-black wavelet

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International Conference on Pattern Recognition

作者： Qingjie Liu Yunhong Wang Zhaoxiang Zhang Lining Liu Intelligent Recognition and Image Processing Lab Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781467322164

In this paper, we propose a new method for remote sensing image pan-sharpening which is based on weighted red-black (WRB) wavelet and adaptive principal component analysis (PCA), where the adaptive PCA is used to reduce spectral distortions and the utilization of WRB wavelet is used to extract the spatial details in PAN images. To reduce the artifacts and spectral distortions in the pan-sharpened images, which were caused by the local instabilities and dissimilarities in the PAN and MS images, a local process strategy incorporating detail enhancement is introduced. The proposed method is tested on two datasets both acquired by QuickBird and compared with the existing methods. Experimental results show that our method can provide promising fused MS images at a high spatial resolution.

关键词： Principal component analysis Correlation Remote sensing Multiresolution analysis Spatial resolution Discrete wavelet transforms

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