Aggregate nearest neighbor(ANN) search retrieves for two spatial datasets T and Q, segment(s) of one or more trajectories from the set T having minimum aggregate distance to points in Q. When interacting with large am...Aggregate nearest neighbor(ANN) search retrieves for two spatial datasets T and Q, segment(s) of one or more trajectories from the set T having minimum aggregate distance to points in Q. When interacting with large amounts of trajectories, this process would be very time-consuming due to consecutive page loads. An approximate method for finding segments with minimum aggregate distance is proposed which can improve the response time. In order to index large volumes of trajectories, scalable and efficient trajectory index(SETI) structure is used. But some refinements are provided to temporal index of SETI to improve the performance of proposed method. The experiments were performed with different number of query points and percentages of dataset. It is shown that proposed method besides having an acceptable precision, can reduce the computation time significantly. It is also shown that the main fraction of search time among load time, ANN and computing convex and centroid, is related to ANN.展开更多
针对基于稀疏不变性假设的单帧超分辨率(SR)算法的局限性,提出一种利用相似最近邻(ANN)统计预测模型的单帧SR算法。首先,利用相似最近邻思想,通过波尔茨曼机捕捉HR字典与LR字典对稀疏模式之间的依赖关系,建立统计预测模型;然后,根据LR块...针对基于稀疏不变性假设的单帧超分辨率(SR)算法的局限性,提出一种利用相似最近邻(ANN)统计预测模型的单帧SR算法。首先,利用相似最近邻思想,通过波尔茨曼机捕捉HR字典与LR字典对稀疏模式之间的依赖关系,建立统计预测模型;然后,根据LR块与HR块相关的最小均方误差(MMSE)计算网络参数,获得它们的依赖关系;最后,利用多层前向神经网络提取字典元素内积,通过计算重叠局部块预测值的均值来重建图像。利用峰值信噪比PSNR和结构相似性度量SSIM评估实验结果,实验结果表明,提出的算法在视觉效果和数值标准方面大多优于其他算法,在选择合适参数情况下,峰值信噪比至少提高0.2 d B。展开更多
文摘Aggregate nearest neighbor(ANN) search retrieves for two spatial datasets T and Q, segment(s) of one or more trajectories from the set T having minimum aggregate distance to points in Q. When interacting with large amounts of trajectories, this process would be very time-consuming due to consecutive page loads. An approximate method for finding segments with minimum aggregate distance is proposed which can improve the response time. In order to index large volumes of trajectories, scalable and efficient trajectory index(SETI) structure is used. But some refinements are provided to temporal index of SETI to improve the performance of proposed method. The experiments were performed with different number of query points and percentages of dataset. It is shown that proposed method besides having an acceptable precision, can reduce the computation time significantly. It is also shown that the main fraction of search time among load time, ANN and computing convex and centroid, is related to ANN.
文摘针对基于稀疏不变性假设的单帧超分辨率(SR)算法的局限性,提出一种利用相似最近邻(ANN)统计预测模型的单帧SR算法。首先,利用相似最近邻思想,通过波尔茨曼机捕捉HR字典与LR字典对稀疏模式之间的依赖关系,建立统计预测模型;然后,根据LR块与HR块相关的最小均方误差(MMSE)计算网络参数,获得它们的依赖关系;最后,利用多层前向神经网络提取字典元素内积,通过计算重叠局部块预测值的均值来重建图像。利用峰值信噪比PSNR和结构相似性度量SSIM评估实验结果,实验结果表明,提出的算法在视觉效果和数值标准方面大多优于其他算法,在选择合适参数情况下,峰值信噪比至少提高0.2 d B。
