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2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Qin, Feiyu Cao, Lumeng Chen, Xuejin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728123455

Semantic segmentation is a fundamental task in indoor scene understanding. Most previous supervised approaches rely on densely annotated image data sets. Due to the limited amount of images with segmentation labels, the performance of existing networks is greatly limited. In this paper, we exploit temporal correlation in video frames to improve the performance and robustness of segmentation networks. Two effective learning strategies are proposed to propagate the information from a few labeled frames to their immediate neighbor frames. First, we scale up training dataset for supervised semantic segmentation networks by generating pseudo ground-truth for neighboring frames from a labeled frame using filtered homography transformation. Furthermore, we introduce a self-supervised loss function to ensure temporal consistency between the segmentation results of adjacent frames. The experimental results demonstrate that our proposed method outperforms state-of-the-art techniques for semantic segmentation on NYU-Depth V2 dataset. © 2019 IEEE.

关键词： Semantic Segmentation

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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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Human Machine Joint Decision Making in Distorted Surveillance Scenario 2

Human Machine Joint Decision Making in Distorted Surveillanc...

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2nd China Symposium on Cognitive Computing and Hybrid Intelligence, CCHI 2019

作者： Liu, Sen Zhao, Shuxin Pang, Yingxue Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728140919

There is plenty of human-machine joint decision-making scenarios in the real world applications, such as driving assistant, suspect identification, medical diagnosis, etc. Existing algorithms propose that machine should give a rejection option when having a high risk or uncertainty score so that the input can be passed to human to make the decision. This is an interesting algorithmic model of human-machine collaboration, but implicitly assumes that humans are more trustworthy than machines. Such an assumption ignores the bias and inconsistency of human, especially in scenarios where machines have superior recognition ability than humans. In this work, we investigate the human-machine joint decision-making problem in distorted surveillance videos, where machines experimentally prove to be comparable to human beings in tolerance to distortion, sometimes even stronger. We propose a new human-machine joint decision-making framework by considering both the confidences of machine and human. To obtain the confidence of human, we build a real-life human decision-making database and propose a deep neural network to estimate human's confidence. Then, confidence alignment method and decision rule are proposed to further output the final decision. Experiments demonstrate that the proposed framework can make less human intervention and more accurate decisions in several human-machine joint decision-making scenarios. © 2019 IEEE.

关键词： Deep neural networks

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Hybrid transform for HEVC-based lossless coding

Hybrid transform for HEVC-based lossless coding

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2014 IEEE International Symposium on Circuits and systems, ISCAS 2014

作者： Chen, Fangdong Zhang, Jinlei Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781479934324

The High Efficiency Video Coding (HEVC) with the transform bypass mode is simple but inefficient for lossless coding. For this reason, we propose a novel transform to further eliminate the redundancy between residues of different blocks in intra prediction. Dependent on intra prediction modes, the proposed transform is adaptable to exploit correlations of residues formed by different modes. In order to accurately obtain parameters of the transform matrix, an approach similar to the Wiener filtering method is adopted. Experimental results show that on top of the lossless coding mode in HEVC, our method offers the performance with a 7.4% bit-rate reduction on average for All Intra Main configuration. Compared with other representative algorithms, our proposal still shows an improvement in the compression ratio, without substantial increases of computational complexity in the encoder or decoder. © 2014 IEEE.

关键词： Forecasting

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Robust sketch-based image retrieval by saliency detection 22nd

Robust sketch-based image retrieval by saliency detection

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Zhang, Xiao Chen, Xuejin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9783319276700

Sketch-based image retrieval (SBIR) has been extensively studied for decades because sketch is one of the most intuitive ways to describe ideas. However, the large expressional gap between hand-drawn sketches and natural images with small-scale complex structures is the fundamental challenge for SBIR systems. We present a novel framework to efficiently retrieve images with a query sketch based on saliency detection. In order to extract primary contours of the scene and depress textures, a hierarchical saliency map is computed for each image. Object contours are extracted from the saliency map instead of the original natural image. Histograms of oriented gradients (HOG) are extracted at multiple scales on a dense gradient field. Using a bag-of-visual-words representation and an inverted index structure, our system efficiently retrieves images by sketches. The experimental results conducted on a dataset of 15 k photographs demonstrate that our method performs well for a wide range of natural scenes. © Springer International Publishing Switzerland 2016.

关键词： Image retrieval

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Edge-Guided Panoramic Video Stitching with Limited Overlap

Edge-Guided Panoramic Video Stitching with Limited Overlap

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2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Xie, Chaoyu Chen, Xuejin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728123455

Video stitching remains a challenging problem in computer vision. In this paper, we propose a novel edge-guided method to stitch multiple videos that have small overlapped regions. Our algorithm consists of three steps: (1) spherical projection of the input video frames based on camera calibration, (2) edge detection and edge-guided feature matching for video registration, and (3) seam optimization to eliminate distortions and ghosts in the composited panoramic videos. The experimental results and user studies demonstrate that our method is robust to videos that have small overlapped regions and produces more visually pleasing panoramic videos than state-of-the-art techniques. © 2019 IEEE.

关键词： Edge detection

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An accurate SAR imaging method based on generalized minimax concave penalty 12

An accurate SAR imaging method based on generalized minimax ...

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12th European Conference on Synthetic Aperture Radar, EUSAR 2018

作者： Wei, Zhonghao Zhang, Bingchen Han, Bing Xu, Zhilin Hong, Wen Wu, YiRong University of Chinese Academy of Science and Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences China Laboratory of Technology in Geo-spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences China Institute of Electronics Chinese Academy of Sciences China

ISBN: (纸本)9783800746361

Sparse signal processing has been applied in synthetic aperture radar (SAR) imaging successfully. As a typical sparse reconstruction model, L1 regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. The underestimation will cause the errors in RCS estimations. In this paper, we present a SAR imaging method based on generalized minimax convex (GMC) penalty, which can avoid the pixel intensity underestimation. The performance of the proposed method is verified using simulations and real data. © VDE VERLAG GMBH Â Berlin Â Offenbach.

关键词： Radar cross section

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Improved rate-distortion optimization algorithms for hevc lossless coding 21

Improved rate-distortion optimization algorithms for hevc lo...

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21st International Conference on MultiMedia Modeling, MMM 2015

作者： Chen, Fangdong Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9783319144443

To avoid distortion, the quantization is not implemented on residues for lossless mode in HEVC. As a result, the conventional lambda model in Rate-Distortion Optimization (RDO), where lambda is related to the quantization parameter (QP), is unreasonable for lossless coding. This paper first demonstrates the role that lambda value plays in the rough RDO of HEVC lossless coding, and a Simulated Annealing algorithm based approach is proposed to obtain the most appropriate lambda for each largest coding unit. Considering the computational complexity, the other simplified method using least square errors prediction is proposed to improve the rough RDO process. Experimental results reveal that on top of the lossless coding mode, the improved method offers the performance with a 1.0%, 1.3% and 1.1% bit-rate reduction on average for Random-access Main, Low-delay B Main and Low-delay P Main configurations, respectively, while brings negligible increases of computational complexity in the encoder. © Springer International Publishing Switzerland 2015.

关键词： Computational complexity

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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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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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