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arXiv 2020年

作者： Zhang, Wen Deng, Lingfei Zhang, Lei Wu, Dongrui the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China the School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

—Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate the learning in a target domain. It is particularly useful when the target domain has very few or no labeled data, due to annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of TL is not always guaranteed. Negative transfer (NT), i.e., leveraging source domain data/knowledge undesirably reduces the learning performance in the target domain, has been a long-standing and challenging problem in TL. Various approaches have been proposed in the literature to handle it. However, there does not exist a systematic survey on the formulation of NT, the factors leading to NT, and the algorithms that mitigate NT. This paper fills this gap, by first introducing the definition of NT and its factors, then reviewing about fifty representative approaches for overcoming NT, according to four categories: secure transfer, domain similarity estimation, distant transfer, and NT mitigation. NT in related fields, e.g., multi-task learning, lifelong learning, and adversarial attacks, are also discussed. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Multi-label inductive matrix completion for joint MGMT and IDH1 status prediction for glioma patients 1

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20th International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2017

作者： Chen, Lei Zhang, Han Thung, Kim-Han Liu, Luyan Lu, Junfeng Wu, Jinsong Wang, Qian Shen, Dinggang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Radiology and BRIC University of North Carolina Chapel Hill United States School of Biomedical Engineering Med-X Research Institute Shanghai Jiao Tong University Shanghai China Department of Neurosurgery Huashan Hospital Fudan University Shanghai China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai China

ISBN: (数字)9783319661858

ISBN: (纸本)9783319661841

MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is laborious, invasive and time-consuming. Accurate presurgical prediction of their statuses based on preoperative imaging data is of great clinical value towards better treatment plan. In this paper, we propose a novel Multi-label Inductive Matrix Completion (MIMC) model, highlighted by the online inductive learning strategy, to jointly predict both MGMT and IDH1 statuses. Our MIMC model not only uses the training subjects with possibly missing MGMT/IDH1 labels, but also leverages the unlabeled testing subjects as a supplement to the limited training dataset. More importantly, we learn inductive labels, instead of directly using transductive labels, as the prediction results for the testing subjects, to alleviate the overfitting issue in small-sample-size studies. Furthermore, we design an optimization algorithm with guaranteed convergence based on the block coordinate descent method to solve the multivariate non-smooth MIMC model. Finally, by using a precious single-center multi-modality presurgical brain imaging and genetic dataset of primary HGG, we demonstrate that our method can produce accurate prediction results, outperforming the previous widely-used single- or multi-task machine learning methods. This study shows the promise of utilizing imaging-derived brain connectome phenotypes for prognosis of HGG in a non-invasive manner. © Springer International Publishing AG 2017.

关键词： Forecasting

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image Fusion Algorithm Based On Orientation Information Motivated Pulse Coupled Neural Networks

Image Fusion Algorithm Based On Orientation Information Moti...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.6

作者： Xiaobo Qu Changwei Hu Jingwen Yan Department of Communication Engineering Xiamen University Xiamen Fujian China Department of Software Engineering Xiamen University Xiamen Fujian China Key Laboratory of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou Guangdong China

Pulse Coupled Neural Networks (PCNN) is a visual cortex-inspired neural networks and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for image processing and successfully employed in image fusion. However, in most PCNN-based fusion algorithms, only single pixel value is input to motivate PCNN neuron. This is not effective enough because humans are often sensitive to features, not only pixel value. In this paper, novel orientation information is considered as features to motivate PCNN. Visual observation and objective performance evaluation criteria demonstrate that the proposed algorithm outperforms typical wavelet-based, lapacian pyramid transform-based and PCNN-based fusion algorithms.

关键词： image fusion Pulse Coupled Neural Networks Orientation information Wavelet transform image processing

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UNQA: Unified No-Reference Quality Assessment for Audio, image, Video, and Audio-Visual Content

arXiv

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arXiv 2024年

作者： Cao, Yuqin Min, Xiongkuo Gao, Yixuan Sun, Wei Lin, Weisi Zhai, Guangtao The Institute of Image Communication and Network Engineering Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

As multimedia data flourishes on the Internet, quality assessment (QA) of multimedia data becomes paramount for digital media applications. Since multimedia data includes multiple modalities including audio, image, video, and audiovisual (A/V) content, researchers have developed a range of QA methods to evaluate the quality of different modality data. While they exclusively focus on addressing the single modality QA issues, a unified QA model that can handle diverse media across multiple modalities is still missing, whereas the latter can better resemble human perception behaviour and also have a wider range of applications. In this paper, we propose the Unified No-reference Quality Assessment model (UNQA) for audio, image, video, and A/V content, which tries to train a single QA model across different media modalities. To tackle the issue of inconsistent quality scales among different QA databases, we develop a multi-modality strategy to jointly train UNQA on multiple QA databases. Based on the input modality, UNQA selectively extracts the spatial features, motion features, and audio features, and calculates a final quality score via the four corresponding modality regression modules. Compared with existing QA methods, UNQA has two advantages: 1) the multi-modality training strategy makes the QA model learn more general and robust quality-aware feature representation as evidenced by the superior performance of UNQA compared to state-of-the-art QA methods. 2) UNQA reduces the number of models required to assess multimedia data across different modalities. and is friendly to deploy to practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Audiovisual

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Fast coding unit depth decision for HEVC

Fast coding unit depth decision for HEVC

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Fangshun Mu Li Song Xiaokang Yang Zhenyi Luo Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Electronics and Information Engineering Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9781479947164

High Efficiency Video Coding (HEVC) achieves high efficiency by introducing a new coding structure in adoption of coding unit (CU), prediction unit (PU) and transform unit (TU). However, it also imposes great computation burden on the mode decision of encoders. In this paper, we propose a fast CU depth decision scheme to reduce the encoder complexity for HEVC. Firstly, the relationship between rate-distortion (R-D) cost and CU depth is explored carefully with Mean Squared Error (MSE) and Number of Encoded Bits (NEB) metrics. Then CU splitting is modeled as a binary classification problem and resolved by an offline trained Support Vector Machine (SVM) model. The experimental results show that the proposed algorithm achieves up to 59% running-time reduction with negligible loss in terms of PSNR and bit rate.

关键词： Encoding Bit rate Support vector machines Classification algorithms Video coding Prediction algorithms Educational institutions

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Vehicle Color Recognition Using Monocular camera

Vehicle Color Recognition Using Monocular camera

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2011年无线通信与信号处理国际会议(WCSP 2011)

作者： Mengjie Yang Xiuchang Zhu Liang Li Guang Han Xiaofei Li Jiangsu Provincial Key Lab of Image Processing and ImageCommunication Nanjing Univ. of P&TNanjing Engineering Research Center of Wideband Wireless Communication and Display Technique Ministry of Ed Engineering Research Center of Wideband WirelessCommunication and Display Technique Ministry ofEduc

This paper introduces a novel moving vehicle color recognition method. By processing videos recorded by monocular camera on traffic, we get single color image of vehicles which are crossed the traffic in any direction. First, paper employed a H-S two dimension histogram method to detect the color of the vehicles, and distinguish red, yellow, green and blue four color vehicles. Due to deal with the vehicles that do not satisfy above 4 colors, Regional Color Judgment method is proposed. This method consists of the following five steps: Firstly, position the color detection region by identifying the travelling direction of the vehicle. Then pick out the candidate color extraction region used to vehicle recognition. At last extract the color of the candidate region and match the color with the templates we predefined, the color of the best fit template is the color of the vehicle.

关键词： vehicle color recognition region choice HSV space vehicle detection

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Interactive object segmentation using iterative adjustable graph cut

Interactive object segmentation using iterative adjustable g...

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IEEE Visual communications and image processing (VCIP)

作者： Ran Shi Zhi Liu Yinzhu Xue Xiang Zhang School of Communication and Information Engineering Shanghai University Shanghai China Key Laboratory of Advanced Display and System Application Ministry of Education Shanghai University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

Interactive object segmentation is widely used for extracting any user-interested objects from natural images. A common problem with many interactive segmentation approaches is that the object segmentation quality is degraded due to inaccurate object/background seeds provided by the user. This paper proposes an iterative adjustable graph cut to efficiently solve this problem. First, object/background seeds are initialized based on the object segmentation result obtained with the user-specified scribbles as the interactive input. Then, an iterative seed adjustment scheme is exploited to correct inaccurate seeds and extract new suitable seeds via graph cut, in which the balancing weight between energy terms are adaptively updated to protect stable seeds and speedup the iteration process. Finally, suitable seeds are obtained and graph cut is used to segment the objects. Experimental results demonstrate the better segmentation performance of our approach even if user provides rather rough seeds.

关键词： Object segmentation image segmentation Reliability Iterative methods Educational institutions image color analysis Error analysis

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Construction and application of Red5 cluster based on OpenStack

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AIP Conference Proceedings 2017年第1期1864卷

作者： Jiaqing Wang Jianxin Song Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210000 China

With the application and development of cloud computing technology in various fields, the resource utilization rate of the data center has been improved obviously, and the system based on cloud computing platform has also improved the expansibility and stability. In the traditional way, Red5 cluster resource utilization is low and the system stability is poor. This paper uses cloud computing to efficiently calculate the resource allocation ability, and builds a Red5 server cluster based on OpenStack. Multimedia applications can be published to the Red5 cloud server cluster. The system achieves the flexible construction of computing resources, but also greatly improves the stability of the cluster and service efficiency.

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Speed up HEVC encoder by precoding with H.264

Speed up HEVC encoder by precoding with H.264

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

作者： Fangshun Mul Li Song Zhenyi Luo Xiangwen Wang Xiaokang Yang Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Electronics and Information Engineering Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9781479942503

The new video coding standard, High Efficiency Video Coding (HEVC), was developed to replace H264/AVC and achieves high efficiency by introducing a new coding structure in adoption of coding unit (CU), prediction unit (PU) and transform unit (TU). However, it also imposes great computation burden on the mode decision of encoders. In this paper, a conceptual HEVC encoder architecture with cascaded H264/AVC encoder is proposed to speed up the CTU splitting process of HEVC encoder. First, the input frame is precoded with an efficient H264/AVC encoder to gather the partition and prediction information of H264/AVC macroblocks (MBs). Then based on the gathered information, CU and PU modes are carefully screened during HEVC encoding. Two schemes are proposed to reduce the CU and PU candidate modes to accelerate the Coding Tree Unit (CTU) splitting process. At last, the HEVC encoder tries all combinations of candidate CU and PU mode to determine the optimal CU, PU and TU mode by computing the rate-distortion (R-D) cost. The experimental results on highly-optimized open source codecs - x264 and x265 show that the proposed framework can effectively reduce encoding time with negligible loss in terms of PSNR and bitrate.

关键词： Video coding Encoding PSNR Bit rate Decision support systems Abstracts Standards

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Performance evaluation of H.265/MPEG-HEVC encoders for 4K video sequences

Performance evaluation of H.265/MPEG-HEVC encoders for 4K vi...

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

作者： Qingxiong Huangyuan Li Song Zhengyi Luo Xiangwen Wang Yanan Zhao Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai China Instiuue of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Electronics and Information Engineering Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9781479942503

The H.265/MPEG-HEVC is the latest video coding standard, which achieves an increase of about 50% in coding efficiency compared to its predecessor H.264/MPEG-AVC. Ever since H.265/MPEG-HEVC was designed to replace almost all existing H.264/ MPEG-AVC codecs, high-resolution video coding beyond High Definition (4K, 8K, etc.) has drawn more attention. On the other hand, it's well known that reference implementation of HEVC codec, HM, acts an important role during standardization, particularly for evaluation of rate distortion performance of different tools. However, HM is far from a practical codec because of very slow coding speed even on modern multi-core computers. Up to now except for HM few comparisons are known about both the coding performance and the coding speed of practical HEVC encoders for high resolution video sequences. To address this issue, this paper conducts a comprehensive evaluation of latest high performance H.265/MPEG-HEVC encoders, including the open source encoder-x265 and the commercial encoder-DivX265, based on default parameters and a new open 4K video database. Furthermore, latest HM and x264 are also included for performance anchors. The experimental results show DivX265 provides average bit-rate savings of 4.79% relative to HM while x265 with default preset achieves an average reduction of 3.21% in terms of BD-BR saving. In addition, different presets of x265 make a good tradeoff between coding speed and R-D performance while DivX265 is almost as fast as x265 ultrafast preset. We believe such evaluation information could provide a more comprehensive picture of state-of-the-art H.265/MPEG-HEVC encoders.

关键词： Transform coding Encoding Decision support systems Codecs Video coding Abstracts Standards

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