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Dual-Graph Regularized Sparse Low-Rank Matrix Recovery for Tag Refinement 14th

Dual-Graph Regularized Sparse Low-Rank Matrix Recovery for T...

引用

14th International conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Sun, Dengdi Bao, Yuanyuan Ge, Meiling Ding, Zhuanlian Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China

ISBN: (纸本)9789811534140

In recent years, extremely large amounts of images with manual tags are easily available in many social websites such as Twitter, Flickr, and Instagram. However, these user-provided tags are often imprecise and incomplete, which inevitably limits the performances of image retrieval and other related applications. To this end, tag refitment technology aims at improving the quality of images tags automatically, and has been a fundamental challenge in Internet era. In this paper, we propose a novel dual-graph regularized sparse low-rank matrix recovery method, referred to as DGSLR briefly, to infer and improve the manual tags. Specifically, our DGSLR model first suppress the sparse noisy tags by norm, and decompose simultaneously the residual low-rank matrix to learning the low-dimensional vector representations of images and tags. Moreover, the visual similarities and the tag pairwise correlation are fully exploited to smooth the decomposition results by using the dual lapalcian graph regularization terms. For optimization, an improved alternative iteration strategy is designed to solve the resulting objective function. Extensive experiments on the tasks of tag refinement demonstrate the superior performance of the proposed algorithms over several representative methods, especially when the manual tag data is highly noisy and sparse. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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A Progressive background updating based coding scheme for surveillance videos 34

A Progressive background updating based coding scheme for su...

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34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Li, Meng Shi, Yunhui Ding, Wenpeng Liu, Xiaojie Liu, Jiaying Yin, Baocai Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing100124 China Baidu Inc China Henan Institute of Technology School of Computer Science and Technology Xinxiang China Peking University China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China

ISBN: (纸本)9781728137230

Background based coding is an effective scheme to improve the coding efficiency of surveillance videos. However, it takes a long time to generate a high quality background picture (BG-picture). And the encoding of the high quality BG-picture will increase the bitrate abruptly. To solve these problems, a progressive background updating based coding scheme is proposed in this paper. In the proposed scheme, the BG-picture is updated block by block. To improve the overall coding efficiency, an importance map is designed to select the valid background blocks (B-blocks) progressively which will be encoded with high quality. It is worth noting that only the valid B-blocks is encoded instead of the entire BG-picture. Compared with the reference software of Versatile Video Coding (VVC), the proposed scheme achieves about 23.3 percent bit-rate saving on average. © 2019 IEEE.

关键词： Video signal processing

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Disparity compensation of light fields for improved efficiency in 4D transform-based encoders

Disparity compensation of light fields for improved efficien...

引用

IEEE visual communications and image processing (VCIP)

作者： João M. Santos Lucas A. Thomaz Pedro A. A. Assuncao Luis A. da Silva Cruz Luis M. N. Tavora Sergio M. M. Faria Instituto de Telecomunicações Portugal University of Coimbra Coimbra Portugal ESTG Polytechnic of Leiria Leiria Portugal

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Efficient light field encoders take advantage of the inherent 4D data structures to achieve high compression performance. This is accomplished by exploiting the redundancy of co-located pixels in different sub-aperture images (SAIs) through prediction and/or transform schemes to find a m ore compact representation of the signal. However, in image regions with higher disparity between SAIs, such scheme's performance tends to decrease, thus reducing the compression efficiency. This paper introduces a reversible pre-processing algorithm for disparity compensation that operates on the SAI domain of light field data. The proposed method contributes to improve the transform efficiency of the encoder, since the disparity-compensated data presents higher correlation between co-located image blocks. The experimental results show significant improvements in the compression performance of 4D light fields, achieving Bjontegaard delta rate gains of about 44% on average for MuLE codec using the 4D discrete cosine transform, when encoding High Density Camera Arrays (HDCA) light field images.

关键词： image coding Cameras Transform coding Codecs Redundancy Prediction algorithms Encoding

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Random-access-aware Light Field Video Coding using Tree Pruning Method

Random-access-aware Light Field Video Coding using Tree Prun...

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IEEE visual communications and image processing (VCIP)

作者： Thuc Nguyen Huu Vinh Van Duong Byeungwoo Jeon Sungkyunkwan University South Korea

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The increasing prevalence of VR/AR as well as the expected availability of Light Field (LF) display soon call for more practical methods to transmit LF image/video for services. In that aspect, the LF video coding should not only consider the compression efficiency but also the view random-access capability (especially in the multi-view-based system). The multi-view coding system heavily exploits view dependencies coming from both inter-view and temporal correlation. While such a system greatly improves the compression efficiency, its view random-access capability can be much reduced due to so called "chain of dependencies." In this paper, we first model the chain of dependencies by a tree, then a cost function is used to assign an importance value to each tree node. By travelling from top to bottom, a node of lesser importance is cut-off, forming a pruned tree to achieve reduction of random-access complexity. Our tree pruning method has shown to reduce about 40% of random-access complexity at the cost of minor compression loss compared to the state-of-the-art methods. Furthermore, it is expected that our method is very lightweight in its realization and also effective on a practical LF video coding system.

关键词： Complexity theory Encoding Decoding Video coding Light fields image coding Correlation

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Orthogonal Coded Multi-view Structured Light for Inter-view Interference Elimination

Orthogonal Coded Multi-view Structured Light for Inter-view ...

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IEEE visual communications and image processing (VCIP)

作者： Zaichao Sun Guochao Qian Zhaoyu Peng Weiju Dai Dongjun Sun Gongyuan Zhang Nongtao Zhang Jun Xu Ren Wang Chunlin Li Electronic Power Research Institute of Yunnan Electronic Power Grid Co. Ltd Kunming China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Rapid 3D reconstruction of dynamic scenes is very useful in 3D object structure analysis, accident avoidance for UAV, and other visual applications. Against dynamic scenes, coded structured light methods have been proposed to obtain the depth information of an object in 3D world, and most of them are based on spatial codification. A brutal truth is that two or more cameras and projectors from different viewpoints are needed to measure the dynamic scene simultaneously for rapid 3D reconstruction. However, when two traditional patterns, especially the binaries, are mutually overlapped, interference between them arises to a new challenge to 3D reconstruction. Traditional patterns can hardly be separated from each other, which surely influence the quality of the 3D reconstruction. To eliminate the interference problem, we propose a scheme of orthogonal coded multi-view structured light systems, which can obtain accurate of depth maps for a scene. Besides, we also test the stability of the orthogonal patterns by establishing three different scenes and making a comparisons to traditional patterns. New state-of-the-art results can be obtained by our scheme in the experiments.

关键词： Interference Cameras Three-dimensional displays Power system dynamics Sun image coding Decoding

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DCM: A Dense-Attention Context Module For Semantic Segmentation

DCM: A Dense-Attention Context Module For Semantic Segmentat...

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IEEE International conference on image processing

作者： Shenghua Li Quan Zhou Jia Liu Jie Wang Yawen Fan Xiaofu Wu Longin Jan Latecki National Engineering Research Center of Communications and Networking Nanjing University of Posts & Telecommunications P.R. China Department of Computer and Information Sciences Temple University Philadelphia USA

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

For image semantic segmentation, a fully convolutional network is usually employed as the encoder to abstract visual features of the input image. A meticulously designed decoder is used to decoding the final feature map of the backbone. The output resolution of backbones which are designed for image classification task is too low to match segmentation task. Most existing methods for obtaining the final high-resolution feature map can not fully utilize the information of different layers of the backbone. To adequately extract the information of a single layer, the multi-scale context information of different layers, and the global information of backbone, we present a new attention-augmented module named Dense-attention Context Module (DCM), which is used to connect the common backbones and the other decoding heads. The experiments show the promising results of our method on Cityscapes dataset.

关键词： Convolution Feature extraction Semantics Data mining image segmentation Kernel Decoding

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Making Large Ensemble of Convolutional Neural Networks via Bootstrap Re-sampling 34

Making Large Ensemble of Convolutional Neural Networks via B...

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34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Li, Jiawei Xiang, Xingchun Dai, Tao Xia, Shu-Tao Tsinghua University Department of Computer Science and Technology Beijing China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781728137230

The ensemble of Convolutional Neural Networks (CNNs) is known to be more accurate and robust than the component CNNs models. Along with the development of a fast training method, current research has managed to make an effective ensemble of several CNNs models and require no additional training cost. However, when the ensemble size of CNNs is further increased, it is hard to observe a corresponding performance enhancement. According to the generalization capability analysis of CNNs, this phenomenon can be explained by the oversaturation of model capacity and the close correlation among the component CNNs, especially when the CNNs are trained within the same dataset. To address this problem, we propose to train CNNs on re-sampled bootstrap datasets. Extensive experiments demonstrate the bootstrap re-sampling is effective for a large ensemble size (up to 80). Besides, benefiting from the usage of the bootstrap re-sampling technique, we can also have an unbiased estimate of the standard deviation of the ensemble output. © 2019 IEEE.

关键词： Convolution

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An Improved Fuzzy Algorithmic Approach Applying on Medical image to Improve the Contrast

An Improved Fuzzy Algorithmic Approach Applying on Medical I...

引用

International Wireless communications and Mobile Computing conference, IWCMC

作者： Min Wu Mathematics and Computer Institute Guangdong Ocean University Zhanjiang China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

Noise led from nodes of medical imaging system and underexposure or overexposure will make the image be in a low contrast. In this paper we try to enhance the image contrast with a method of digital image processing. We present an improved fuzzy algorithmic approach on the basis of the conventional Pas. S. K algorithm. Processed by this improved algorithm, the imperceptible information can be avoided to lose when the planar space of original image is transformed to the fuzzy space. And special experiment has been carried out to demonstrate the effect of this approach applying on the X-ray roentgenoscopy image of animal carotid, which is obtained by a homemade instrument and convert to digital signal with an image-collecting card. We find that both images processed with the conventional algorithm and the improved algorithm can provide more details than the original one. While percent of the zero gray pixels of the image processed with the conventional algorithm is much more (to reach 12.3%) than that with the improved algorithm (6.2%) by analyzing with visual C++ image process program, indicating a more loss of information caused by the forcible process of making some image elements equal to zero. Thus it can be concluded that the improved algorithm seems to be more distinct than the conventional one.

关键词： Biomedical imaging image enhancement image quality Histograms Biomedical image processing Fuzzy sets Manganese

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Learning Matching Behavior Differences for Compressing Vehicle Re-identification Models

Learning Matching Behavior Differences for Compressing Vehic...

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IEEE visual communications and image processing (VCIP)

作者： Yi Xie Jianqing Zhu Huanqiang Zeng Canhui Cai Lixin Zheng College of Engineering Huaqiao University China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Vehicle re-identification matching vehicles captured by different cameras has great potential in the field of public security. However, recent vehicle re-identification approaches exploit complex networks, causing large computations in their testing phases. In this paper, we propose a matching behavior difference learning (MBDL) method to compress vehicle re-identification models for saving testing computations. In order to represent the matching behavior evolution across two different layers of a deep network, a matching behavior difference (MBD) matrix is designed. Then, our MBDL method minimizes the L1 loss function among MBD matrixes from a small student network and a complex teacher network, ensuring the student network use less computations to simulate the teacher network's matching behaviors. During the testing phase, only the small student network is utilized so that testing computations can be significantly reduced. Experiments on VeRi776 and VehicleID datasets show that MBDL outperforms many state-of-the-art approaches in terms of accuracy and testing time performance.

关键词： Training Testing Probes image coding Loss measurement Computational modeling Trajectory

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Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy 34

Improving Person Re-Identification Performance Using Body Ma...

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34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Wu, Junyi Yao, Lingxiang Huang, Yan Xu, Jingsong Wu, Qiang Huang, Liqin Fuzhou University College of Physics and Information Fuzhou China University of Technology Sydney School of Electrical and Data Engineering Sydney Australia

ISBN: (纸本)9781728137230

The task of person re-identification (re-id) is to find the same pedestrian across non-overlapping cameras. Normally, the performance of person re-id can be affected by background clutters. However, existing segmentation algorithms are hard to obtain perfect foreground person images. To effectively leverage the body (foreground) cue, and in the meantime pay attention to discriminative information in the background (e.g., companion or vehicle), we propose to use a cross-learning strategy to take both foreground and other discriminative information into account. In addition, since currently existing foreground segmentation result always involves noise, we use Label Smoothing Regularization (LSR) to strengthen the generalization capability during our learning process. In experiments, we pick up two state-of-The-Art person re-id methods to verify the effectiveness of our proposed cross-learning strategy. Our experiments are carried out on two publicly available person re-id datasets. Obvious performance improvements can be observed on both datasets. © 2019 IEEE.

关键词： image segmentation

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