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Learning a saliency evaluation metric using crowdsourced perceptual judgments

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arXiv 2018年

作者： Xia, Changqun Li, Jia Su, Jinming Borji, Ali State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China International Research Institute for Multidisciplinary Science Beihang University Beijing China Center for Research in Computer Vision Computer Science Department University of Central Florida OrlandoFL United States

In the area of human fixation prediction, dozens of computational saliency models are proposed to reveal certain saliency characteristics under different assumptions and definitions. As a result, saliency model benchmarking often requires several evaluation metrics to simultaneously assess saliency models from multiple perspectives. However, most computational metrics are not designed to directly measure the perceptual similarity of saliency maps so that the evaluation results may be sometimes inconsistent with the subjective impression. To address this problem, this paper first conducts extensive subjective tests to find out how the visual similarities between saliency maps are perceived by humans. Based on the crowdsourced data collected in these tests, we conclude several key factors in assessing saliency maps and quantize the performance of existing metrics. Inspired by these factors, we propose to learn a saliency evaluation metric based on a two-stream convolutional neural network using crowdsourced perceptual judgements. Specifically, the relative saliency score of each pair from the crowdsourced data is utilized to regularize the network during the training process. By capturing the key factors shared by various subjects in comparing saliency maps, the learned metric better aligns with human perception of saliency maps, making it a good complement to the existing metrics. Experimental results validate that the learned metric can be generalized to the comparisons of saliency maps from new images, new datasets, new models and synthetic data. Due to the effectiveness of the learned metric, it also can be used to facilitate the development of new models for fixation prediction. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

Crowd behavior simulation with emotional contagion in unexpected multi-hazard situations

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arXiv 2018年

作者： Xu, Mingliang Xie, Xiaozheng Lv, Pei Niu, Jianwei Wang, Hua Li, Chaochao Zhu, Ruijie Deng, Zhigang Zhou, Bing Center for Interdisciplinary Information Science Research ZhengZhou University 450000 State Key Laboratory of Virtual Reality Technology and Systems Beihang University 100091 School of Computer and Communication Engineering of Zhengzhou University of Light Industry 450000 Department of Computer Science University of Houston HoustonTX77204-3010

Numerous research efforts have been conducted to simulate crowd movements, while relatively few of them are specifically focused on multi-hazard situations. In this paper, we propose a novel crowd simulation method by modeling the generation and contagion of panic emotion under multi-hazard circumstances. In order to depict the effect from hazards and other agents to crowd movement, we first classify hazards into different types (transient and persistent, concurrent and non-concurrent, static and dynamic) based on their inherent characteristics. Second, we introduce the concept of perilous field for each hazard and further transform the critical level of the field to its invoked-panic emotion. After that, we propose an emotional contagion model to simulate the evolving process of panic emotion caused by multiple hazards. Finally, we introduce an Emotional Reciprocal Velocity Obstacles (ERVO) model to simulate the crowd behaviors by augmenting the traditional RVO model with emotional contagion, which for the first time combines the emotional impact and local avoidance together. Our experiment results demonstrate that the overall approach is robust, can better generate realistic crowds and the panic emotion dynamics in a crowd. Furthermore, it is recommended that our method can be applied to various complex multi-hazard environments. Copyright © 2018, The Authors. All rights reserved.

关键词： Hazards

Research on the influence factors of consumer repurchase in Dutch auction

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Research on the influence factors of consumer repurchase in ...

2018中国信息通信大会(CICC2018)

作者： JIANG Wei-Jin JIANG Yi-Rong CHEN Jia-Hui XU Yu-Hui WANG Yang TAN Li-Na School of Computer Science and Technology Wuhan University of Technology Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Institute of Big Data and Internet Innovation Mobile E-business Collaborative Innovation Center of Hunan Province Hunan University of Commerce Tonghua Normal University

With the rapid development of e-commerce,the online shopping market becomes more and more *** to design sales models to retain consumers and make them become continuous buying is the biggest concern of most e-commerce *** study was under the situation of Dutch auction model,using a large number of real auction data in Gongtianxia’s "7 days auction" and "15 minutes auction".We use quantitative research method combining statistical analysis and panel data regression model,to study the influencing factors of consumers’ repeat *** results show that auction methods,consumers’ perceived performance and bidding structure can affect consumers *** the same time,we established random effects regression models of the rate of consumers repurchase in 7 days and 30 *** to the conclusion of the research in this article,we can provide some practical suggestions for Gongtianxia and a reference for other e-commerce.

关键词： Dutch Auction Continuous purchases Panel data Random effects model

Haptics-equiped interactive PCI simulation for patient-specific surgery training and rehearsing

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Science China(Information Sciences) 2016年第10期59卷 233-238页

作者： Shuai LI Qing XIA Aimin HAO Hong QIN Qinping ZHAO State Key Laboratory of Virtual Reality Technology and Systems Beihang University Department of Computer Science Stony Brook University

Despite the long history of medical simulations, suffering from the patient-specific heterogeneous heart physiological structure and complex intravascular procedures, it is still challenging for patient-specific percutaneous coronary intervention(PCI) surgery simulation. In this paper, we advocate a haptics-equiped interactive prototype system towards PCI surgeons training and patient-specific surgery rehearsing, which can afford trainees the opportunity to approximately experience the entire PCI procedures and customized emergency cases that might occur in common clinical settings. The full simulation covers tissue deformation, catheter and wire simulation, X-ray simulation, haptic feedback, and 3D realistic rendering, which in all give rise to the integrated physical, visual, haptic, and procedural realism. Our system can accommodate various comprehensive operations involved in PCI-related procedures, including feeding wires, releasing stents, injecting contrast medium, simulating X-ray, bleeding, etc. Moreover, our system framework is fully built upon CUDA, and thus can achieve real-time performance even on a common desktop. The high-fidelity, real-time efficiency and stableness of our system show great potentials for its practical applications in clinical training fields.

关键词： percutaneous coronary intervention(PCI) PCI surgery simulation patient-specific surgery rehearsing physically-based modeling haptics

Complementary Segmentation of Primary Video Objects with Reversible Flows

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arXiv 2018年

作者： Li, Jia Wu, Junjie Zheng, Anlin Song, Yafei Zhang, Yu Chen, Xiaowu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China National Engineering Laboratory for Video Technology School of EE&CS Peking University Beijing100871 China A.I. Labs Alibaba Group Beijing100102 China SenseTime Group Limited 100084 China

—Segmenting primary objects in a video is an important yet challenging problem in computer vision, as it exhibits various levels of foreground/background ambiguities. To reduce such ambiguities, we propose a novel formulation via exploiting foreground and background context as well as their complementary constraint. Under this formulation, a unified objective function is further defined to encode each cue. For implementation, we design a Complementary Segmentation Network (CSNet) with two separate branches, which can simultaneously encode the foreground and background information along with joint spatial constraints. The CSNet is trained on massive images with manually annotated salient objects in an end-to-end manner. By applying CSNet on each video frame, the spatial foreground and background maps can be initialized. To enforce temporal consistency effectively and efficiently, we divide each frame into superpixels and construct neighborhood reversible flow that reflects the most reliable temporal correspondences between superpixels in far-away frames. With such flow, the initialized foregroundness and backgroundness can be propagated along the temporal dimension so that primary video objects gradually pop-out and distractors are well suppressed. Extensive experimental results on three video datasets show that the proposed approach achieves impressive performance in comparisons with 18 state-of-the-art models. Copyright © 2018, The Authors. All rights reserved.

关键词： Motion compensation

LEARNING FROM WEAKLY-LABELED CLINICAL DATA FOR AUTOMATIC THYROID NODULE CLASSIFICATION IN ULTRASOUND IMAGES

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LEARNING FROM WEAKLY-LABELED CLINICAL DATA FOR AUTOMATIC THY...

IEEE International Conference on Image Processing

作者： Jianxiong Wang Shuai Li Wenfeng Song Hong Qin Bo Zhang Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science Stony Brook University Stony Brook USA Department of Ultrasound Chinese Academy of Medical Science & Peking Union Medical College Hospital Beijing China

This paper proposes a semi-supervised learning method based on weakly-labeled data to automatically classify ultrasound (US) thyroid nodules. key to our new approach is the unification of multi-instance learning (MIL) with deep learning. Benefiting from that, our method can directly use off-the-shelf clinical data, which involves no labels to indicate nodule classes. To this end, we take the US images of a patient as a bag, and take the corresponding pathology report as the bag label. Specifically, we first propose a bag generating method, wherein the detected thyroid nodules are considered as instances corresponding to certain bag. After that, we design an effective EM algorithm to train a convolutional neural network (CNN) for nodule classification. We conduct extensive experiments and comprehensive evaluations on different datasets, and all the experiments confirm that, our method significantly outperforms state-of-the-art MIL algorithms, which exhibits great potential in clinical applications.

关键词： Cancer Proposals Image edge detection Training Biomedical imaging Feature extraction Ultrasonic imaging

Dynamic community mining based on behavior prediction

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International Journal of Performability Engineering 2018年第7期14卷 1590-1599页

作者： Chen, Xiao Hu, Xinzhuan Pan, Xiao Guo, Jingfeng Network Technology Center Hebei Normal University of Science and Technology Qinhuangdao066004 China College of Economics and Management YanShan University Qinhuangdao066004 China College of Economic and Management Shijiazhuang Tiedao University Shijiazhuang050043 China College of Information Science and Engineering YanShan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China

Dynamic network research has been a new trend in recent years. Based on the influence of vertex behavior on community structure, this paper studies signed network dynamic community mining. Firstly, the set pair connection degree is introduced to describe the relation between vertices, and the edge prediction model of signed network is proposed by taking into account the variability of the relation between vertices. Secondly, based on the prediction model, a set pair signed networks dynamic model is proposed by adding time axis T to the signed network. Then, based on the dynamic model, the evolution of signed networks and community discovering are studied. Finally, network evolution law and community stability are analyzed by using the connection trend and connection entropy in set pair theory, and the accuracy and validity of the dynamic community mining algorithm are verified by experiments. © 2018 Totem Publishers Ltd. All rights reserved.

关键词： Dynamic models

How drones look: Crowdsourced knowledge transfer for aerial video saliency prediction

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arXiv 2018年

作者： Fu, Kui Li, Jia Shen, Hongze Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China International Research Institute for Multidisciplinary Science Beihang University Beijing100191 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Cooperative Medianet Innovation Center China

In ground-level platforms, many saliency models have been developed to perceive the visual world as the human does. However, they may not fit a drone that can look from many abnormal viewpoints. To address this problem, this paper proposes a Crowdsourced Multi-path Network (CMNet) that transfer the ground-level knowledge for spatiotemporal saliency prediction in aerial videos. To train CMNet, we first collect and fuse the eye-tracking data of 24 subjects on 1,000 aerial videos to annotate the ground-truth salient regions. Inspired by the crowdsourced annotation in eye-tracking experiments, we design a multi-path architecture for CMNet, in which each path is initialized under the supervision of a classic ground-level saliency model. After that, the most representative paths are selected in a data-driven manner, which are then fused and simultaneously fine-tuned on aerial videos. In this manner, the prior knowledge in various classic ground-level saliency models can be transferred into CMNet so as to improve its capability in processing aerial videos. Finally, the spatial predictions given by CMNet are adaptively refined by incorporating the temporal saliency predictions via a spatiotemporal saliency optimization algorithm. Experimental results show that the proposed approach outperforms ten state-of-the-art models in predicting visual saliency in aerial videos. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

Building a Chinese discourse topic corpus with a micro-topic scheme based on theme-rheme theory

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Big Data Analytics 2017年第1期2卷 1-14页

作者： Xi, Xue-feng Zhou, Guodong Department of Computer Science and Technology Suzhou University of Science and Technology Suzhou China School of Computer Science and Technology Soochow University Suzhou China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou China

How to build a suitable discourse topic structure is an important issue in discourse topic analysis, which is the core of natural language understanding. Not only is it the key basic unit to implement automatic computing, but also the key to realize the transformation from unstructured data to structured data during the process of big data analytics. Although the discourse topic structure has wide potential for application in discourse analysis and related tasks, the research on constructing such discourse resources is quite limited in Chinese language. In this paper, we propose a micro-topic scheme (MTS) to represent the discourse topic structure in the Chinese language according to theme-rheme theory, with elementary discourse topic unit(EDTU) as the node and referent of theme-rheme as link. In particular, thematic progression is employed to directly represent the development of the discourse topic structure. Guided by the MTS, we manually annotate a Chinese Discourse Topic Corpus (CDTC) of 500 documents. Moreover, we get 89.9 and 72.15 F1 value in two identification preliminary experiments, respectively, which show that the proposed representation can perform good automatic computation. The lack of the formal representation system and related corpus resources for Chinese discourse topic structure has greatly restricted the study of discourse topic analysis in natural language, and further affected the development of natural language understanding. To address the above issues, a micro-topic scheme(MTS) representation is proposed based on functional grammar theory, and the corresponding corpus resources(i.e., CDTC) are constructed. Our preliminary evaluation justifies the appropriateness of the MTS for Chinese discourse analysis and the usefulness of our CDTC.

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