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25th ACM International Conference on Multimedia, MM 2017

作者： Wang, Min Zhou, Wengang Tian, Qi Pu, Junfu Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China Computer Science Department University of Texas San Antonio United States

ISBN: (纸本)9781450349062

Approximate Nearest Neighbour (ANN) search is an important research topic in multimedia and computer vision fields. In this paper, we propose a new deep supervised quantization method by Self-Organizing Map (SOM) to address this problem. Our method integrates the Convolutional Neural Networks (CNN) and Self-Organizing Map into a unified deep architecture. The overall training objective includes supervised quantization loss and classification loss. With the supervised quantization loss, we minimize the differences on the maps between similar image pairs, and maximize the differences on the maps between dissimilar image pairs. By optimization, the deep architecture can simultaneously extract deep features and quantize the features into the suitable nodes in the Self-Organizing Map. The experiments on several public standard datasets prove the superiority of our approach over the existing ANN search methods. Besides, as a byproduct, our deep architecture can be directly applied to classification task and visualization with little modification, and promising performances are demonstrated on these tasks in the experiments. © 2017 ACM.

关键词： Self organizing maps

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Social Diffusion Analysis with Common-Interest Model for Image Annotation

Social Diffusion Analysis with Common-Interest Model for Ima...

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作者： Lei, Chenyi Liu, Dong Li, Weiping Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Automatic image annotation has been extensively studied, mostly from a content-based approach, whose effectiveness is restricted by the 'semantic gap' between low-level image features and semantic annotations, and by the irrelevance of annotations to image content. We propose a social diffusion analysis approach to image annotation, which exploits abundant social diffusion records about how images are disseminated within online social networks. Specifically, we propose a common-interest model to analyze social diffusion records, with the assumption that the diffusion pattern of an image in social networks is highly related to the relevance between image annotations and user preferences. In our proposed model, user preferences are represented as common interests of pairwise users instead of individual user interests. We find the notion of common interests not only facilitates the analysis of social diffusion patterns, but also leads to more accurate profiling of user preferences compared to individual interests. Based on the common-interest model, we design an image annotation framework via social diffusion analysis, which consists of the mining of common interests from social diffusion records, the feature extraction from diffusion graphs and common interests, and the automatic annotation by the learning-to-rank method. Experimental results on real-world data sets show that our proposed common-interest based approach outperforms individual-interest based methods, and also achieves superior performance than state-of-the-art content-based image annotation methods. © 2016 IEEE.

关键词： Diffusion

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An interactive system for eficient 3D furniture arrangement

An interactive system for eficient 3D furniture arrangement

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2017 Computer Graphics International Conference, CGI 2017

作者： Yan, Meng Chen, Xuejin Zhou, Jie CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Dept 6 P.O. Box 4 Hefei Anhui230026 China

ISBN: (纸本)9781450352284

We present an interactive example-based system for non-expert users to generate 3D indoor scenes intuitively. From a set of examples of an interior scene, we extract furniture layout constraints including pairwise and group relationships, ergonomic factors and user habits. Instead of inserting a single furniture object to a visual pleasing and functional position independently, we take advantages of manipulating a group of functionality-related furniture objects for smart editing. To deal with the arrangement problem which requires jointly optimizing a variety of functional and visual criteria, we introduce an efficient two-step pipeline consisting of rough arrangement and layout refinement. In the rough arrangement step, furniture objects are roughly placed according to pairwise relationships for accelerating the further optimization process. In the layout refinement step, we optimize the rough furniture layout to a semantical and functional layout considering spatial relationships, and ergonomic factors and avoiding collisions. User interactions are also allowed to move a specific object or a structural group while our system automatically refines the entire layout. The experimental results demonstrate that our system for scene generation measurably increases the visual quality and time efficiency compared with the completely manual scene editing or modeling. © 2017 ACM.

关键词： Virtual reality

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Nonparametric statistics inspired similarity measure for accuracy in collaborative filtering

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Journal of Computational Information Systems 2013年第20期9卷 8397-8406页

作者： Liu, Shichen Xiong, Yan Liu, Qingwen Qi, Xiang Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China

Similarity computation is especially significant in collaborative filtering algorithms. In the existed literatures or large recommender systems, researchers generally use cosine similarity or Pearson correlation coefficient to compute the similarities. However, on one hand, both two measures are more applicable in linear space while the ratings cannot linearly reflect users' interest intensity. On the other hand, different systems have different rating restricts, so it's unreasonable to treat varied systems with the same computation method. In this work, inspired by nonparametric statistics, a sound memory-based method with a novel similarity measure is proposed to eliminate the affection of rating distributions and system diversities on similarity computation. Experiments based on Jester are set to evaluate the performance of the approach. And the highly competitive results show that our approach is able to significantly improve the recommendation quality. © 2013 Binary Information Press.

关键词： Collaborative filtering

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BURST-LDA: A NEW TOPIC MODEL FOR DETECTING BURSTY TOPICS FROM STREAM TEXT

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Journal of Electronics(China) 2014年第6期31卷 565-575页

作者： Qi Xiang Huang Yu Chen Ziyan Liu Xiaoyan Tian Jing Huang Tinglei Wang Hongqi Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences

Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty topics that experience a sudden increase during a period of time. In this paper, we propose a new topic model named Burst-LDA, which simultaneously discovers topics and reveals their burstiness through explicitly modeling each topic's burst states with a first order Markov chain and using the chain to generate the topic proportion of documents in a Logistic Normal fashion. A Gibbs sampling algorithm is developed for the posterior inference of the proposed model. Experimental results on a news data set show our model can efficiently discover bursty topics, outperforming the state-of-the-art method.

关键词： Text mining Burst detection Topic model Graphical model Bayesian inference

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A Deep Learning Model for Correcting Nonlinear Biases Between the TEC Measurements of COSMIC-2 and GNSS

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Advances in Space Research 2025年

作者： Wang Li Jitong Li Xiangzhou Meng Dongsheng Zhao Changyong He Xiaoqing Zuo Kefei Zhang Faculty of Land Resources Engineering Kunming University of Science and Technology Kunming 650093 China Yunnan Province Key Laboratory of Intelligent Monitoring of Natural Resources and Spatiotemporal Big Data Governance (Under Preparation) Kunming 650051 China School of Environmental Science and Spatial Informatics China University of Mining and Technology Xuzhou 221116 China National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology Hunan University of Science and Technology Xiangtan 411201 China

Radio occultation technology, with its characteristics of global coverage, all-weather capability, and high resolution, has become an important tool for ionospheric research and modeling. However, the nonlinear biases between space-borne and ground-based systems limit the accuracy of data fusion. To solve this problem, a deep learning-based calibration model containing various kinds of parameters is developed for correcting the nonlinear biases. The correcting performances of this model are comprehensively evaluated depending on season and latitude under quiet periods and geomagnetic storms. The results indicate that the model significantly reduces the discrepancies in TEC measurements between COSMIC-2 and GNSS. During the quiet period, the most significant improvement occurred in the (40°S - 20°S) region, where MAE and RMSE were reduced to 0.74 TECU and 1.11 TECU, respectively. This was followed by the (20°S - 0°) and (0° - 20°N) regions, with reductions of 0.83 and 0.93 TECU, and 1.29 and 1.35 TECU, respectively. The weakest improvement was observed in the (20°N - 40°N) region, where MAE and RMSE were 0.92 TECU and 1.36 TECU, respectively. Seasonally, the model performed best in winter, followed by summer, with slightly lower performance in spring and autumn. During the geomagnetic storm period, the best performance was achieved in the (40°S - 20°S) region, with an RMSE of 0.99 TECU and a correlation coefficient of 0.9958. This was followed by the (0° - 20°N) and (20°N - 40°N) regions, which had RMSE values of 1.35 and 1.40 TECU and correlation coefficients of 0.9942 and 0.9940, respectively. The weakest performance occurred in the (20°S - 0°) region, with an RMSE of 1.51 TECU and a correlation coefficient of 0.9937. These findings demonstrate that the proposed model significantly enhances the accuracy of occultation data, providing valuable insights for ionospheric modeling and data integration.

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TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Lin, Jianxin Pang, Yingxue Xia, Yingce Chen, Zhibo Luo, Jiebo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China University of Rochester Rochester United States

ISBN: (数字)9783030585488

ISBN: (纸本)9783030585471

An unsupervised image-to-image translation (UI2I) task deals with learning a mapping between two domains without paired images. While existing UI2I methods usually require numerous unpaired images from different domains for training, there are many scenarios where training data is quite limited. In this paper, we argue that even if each domain contains a single image, UI2I can still be achieved. To this end, we propose TuiGAN, a generative model that is trained on only two unpaired images and amounts to one-shot unsupervised learning. With TuiGAN, an image is translated in a coarse-to-fine manner where the generated image is gradually refined from global structures to local details. We conduct extensive experiments to verify that our versatile method can outperform strong baselines on a wide variety of UI2I tasks. Moreover, TuiGAN is capable of achieving comparable performance with the state-of-the-art UI2I models trained with sufficient data. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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NON-PARAMETRIC TOPIC MODEL FOR DISCOVERING geoGRAPHICAL TOPIC VARIATIONS

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Journal of Electronics(China) 2014年第6期31卷 576-586页

作者： Qi Xiang Huang Yu Song Jun Huang Tinglei Wang Hongqi Fu Kun Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences

This paper presents a non-parametric topic model that captures not only the latent topics in text collections, but also how the topics change over space. Unlike other recent work that relies on either Gaussian assumptions or discretization of locations, here topics are associated with a distance dependent Chinese Restaurant Process(ddC RP), and for each document, the observed words are influenced by the document's GPS-tag. Our model allows both unbound number and flexible distribution of the geographical variations of the topics' content. We develop a Gibbs sampler for the proposal, and compare it with existing models on a real data set basis.

关键词： Text mining Topic model geographical topics Bayesian non-parameter

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Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data

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Frontiers of Information technology & Electronic Engineering 2016年第5期17卷 403-412页

作者： Xiu-rui GENG Lu-yan JI Kang SUN Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China MOE Key Laboratory for Earth System Modeling Center for Earth System Science Tsinghua University Beijing 100084 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang 050000 China

Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise(outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis(PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF'(PCNMF). Experimental results show that PCNMF is both accurate and time-saving.

关键词： Non-negative matrix factorization(NMF) Principal component analysis(PCA) Endmember Hyperspectral

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Construction and analysis of long-term surface temperature dataset in Fujian Province

Construction and analysis of long-term surface temperature d...

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ISPRS geospatial Week 2017

作者： Li, W.E. Wang, X.Q. Su, H. Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education National Engineering Research Centre of Geo-spatial Information Technology Fuzhou University China

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the splitwindow algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province. © Authors 2017. CC BY 4.0 License.

关键词： Remote sensing

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