ID 原文 译文
22995 针对具有多模分布结构的高维数据的分类问题，该文提出一种无限最大间隔线性判别投影(i MMLDP)模型。 An infinite Max-Margin Linear Discriminant Projection (iMMLDP) model is developed to deal with the classification problem on multimodal distributed high-dimensional data.
22996 与现有全局投影方法不同，模型通过联合Dirichlet过程及最大间隔线性判别投影(MMLDP)模型将数据划分为若干个局部区域，并在每一个局部学习一个最大边界线性判别投影分类器。 Different from global projection, i MMLDP divides the data into a set of local regions via Dirichlet Process (DP) mixture model and meanwhile learns a linear Max-Margin Linear Discriminant Projection (MMLDP) classifier in each local region.
22997 组合各局部分类器，实现全局非线性的投影与分类。 By assembling these local classifiers, a flexible nonlinear classifier is constructed.
22998 i MMLDP模型利用贝叶斯框架联合建模，将聚类、投影及分类器进行联合学习，可以有效发掘数据的隐含结构信息，因而，可以较好地对非线性可分数据，尤其是具有多模分布特性数据进行分类。 Under this framework, iMMLDP combines dimensionality reduction, clustering and supervised classification in a principled way, therefore, an underlying structure of the data could be uncovered. As a result, the model can handle the classification of data with global nonlinear structure, especially the data with multi-modally distributed structure.
22999 得益于非参数贝叶斯先验技术，可以有效避免模型选择问题，即局部区域划分数量。 With the help of Bayesian nonparametric prior, the model selection problem (e.g. the number of local regions) can be avoided.
23000 基于仿真数据集、公共数据集及雷达实测数据集验证了所提方法的有效性。 The proposed model is implemented on synthesized and real-world data, including multi-modally distributed datasets and measured radar high range resolution profile (HRRP) data, to validate its efficiency and effectiveness.
23001 随着深度特征在图像显著检测领域中发挥越来越重要的作用，传统的 RGB 图像显著检测模型由于未能充分利用深度信息已经不能适用于 RGB-D 图像的显著检测。 Along with more and more important role of depth features played in computer saliency community, traditional RGB saliency models can not directly utilized for saliency detection on RGB-D domains.
23002 该文提出显著中心先验和显著-深度(S-D)概率矫正的RGB-D 显著检测模型，使得深度特征和 RGB 特征间相互指导，相互补充。 This paper proposes saliency center prior and Saliency-Depth (S-D) probability  adjustment RGB-D saliency detection framework, making the depth and RGB features adaptively fuse and complementary to each other.
23003 其次，采用特征融合的流形排序算法获取 RGB 图像的初步显著图。 First, the initial saliency maps of depth images are obtained according to three-dimension space weights and depth prior;
23004 接着，计算基于深度的显著中心先验，并以该先验作为显著权重进一步提升 RGB 图像的显著检测结果，获取 RGB 图像最终显著图； second, the feature fused Manifold Ranking model with extracted depth features is utilized for RGB image saliency detection. Then, the saliency center prior based on depth is computed and this value is used as saliency weight to further improve the RGB image saliency detection results, obtaining the final RGB saliency map.