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 natu...
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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 step...
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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 ...
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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...
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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.
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 assumpt...
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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.
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 quantiza...
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Named Data networking(NDN) has emerged as a new communication paradigm designed for efficient dissemination of data. However, mobility issues are not considered sufficiently. Consumer or producer mobility can incur re...
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ISBN:
(纸本)9781479947249
Named Data networking(NDN) has emerged as a new communication paradigm designed for efficient dissemination of data. However, mobility issues are not considered sufficiently. Consumer or producer mobility can incur request staleness issue, the loss of Interest and Data packets and communication delay. Through analysis, we consider how to forward the buffered data from old access point(AP) to new one and how to keep routing consistency during handoff stage are two key points to address mobility issue in NDN. To minimize the loss of Interest and Data,handoff delay during moving, we design a mobility support architecture(MobiNDN), which is a centralized system architecture and consists of initialization and three stages:registration,deletion and updating. We design three mobility scenarios and schemes and evaluate the performance of MobiNDN by comparing it against exiting NDN using extensive ndnSIM simulation. Our simulation results clearly show that MobiNDN architecture effectively decreases handoff delay and receives data packets during/after handoffs comparing with the exiting NDN.
There are more and more pervasive applications of Wireless Multicast Network, such as the video conference, the voice transmission and the software updates etc. The traditional wireless multicast routing method is to ...
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ISBN:
(纸本)9781479947249
There are more and more pervasive applications of Wireless Multicast Network, such as the video conference, the voice transmission and the software updates etc. The traditional wireless multicast routing method is to construct a multicast tree,which may ignore many available links. This paper presents Markov Decision Process Based Multicast Opportunistic Routing Model. Whether each node of the whole network has received a packet stands for different states of a Markov chains. That is to say that Markov state transition is a routing process. This paper provides a new approach for the study of wireless multicast networks routing. In the reward function of the model, the consumption of Request and ACK among the nodes is abundantly considered. In accordance with each state of the network, the network chooses the optimal forwarding nodes which make the reward function of the model maximum. The experiment and the simulation results proves MDP is most effective routing scheme,and meets with the network system's requirement.
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 dimension...
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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.
The ambiguous Doppler centroid causes incorrect estimation result of the radial velocity. For moving targets with fast radial velocity, an unambiguous estimation approach of the radial velocity is introduced for the a...
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