ID 原文 译文
19545 显著性目标检测旨在于一个场景中自动检测能够引起人类注意的目标或区域，在自底向上的方法中，基于多核支持向量机(SVM)的集成学习取得了卓越的效果。 Salient object detection which aims at automatically detecting what attracts human’s attention most in a scene, bootstrap learning based on Support Vector Machine(SVM) has achieved excellent performance in bottom-up methods.
19546 然而，针对每一张要处理的图像，该方法都要重新训练，每一次训练都非常耗时。 However, it is time-consuming for each image to be trained once based on multiple kernelSVM ensemble.
19547 因此，该文提出一个基于加权的K近邻线性混合(WKNNLB)显著性目标检测方法： So a salient object detection model via Weighted K-Nearest Neighbor Linear Blending(WKNNLB) is proposed.
19548 利用现有的方法来产生初始的弱显著图并获得训练样本，引入加权的K近邻(WKNN)模型来预测样本的显著性值，该模型不需要任何训练过程，仅需选择一个最优的K值和计算与测试样本最近的K个训练样本的欧式距离。 First of all, existing saliency detection methods are employed to generate weaksaliency maps and obtain training samples. Then, Weighted K-Nearest Neighbor (WKNN) is introduced tolearning salient score of samples. WKNN model needs no pre-training process, only needs selecting K value andcomputing saliency value by the K-nearest neighbors labels of training sample and the distances between the K-nearest neighbors training samples and the testing sample.
19549 为了减少选择K值带来的影响，多个加权的K近邻模型通过线性混合的方式融合来产生强的显著图。 In order to reduce the influence of selecting K value,linear blending of multi-WKNNs is applied to generating strong saliency maps.
19550 最后，将多尺度的弱显著图和强显著图融合来进一步提高检测效果。 Finally, multi-scale saliencymaps of weak and strong model are integrated together to further improve the detection performance.
19551 在常用的ASD和复杂的DUT-OMRON数据集上的实验结果表明了该算法在运行时间和性能上的有效性和优越性。 The experimental results on common ASD and complex DUT-OMRON datasets show that the algorithm is effective and superior in running time and performance.
19552 当采用较好的弱显著图时，该算法能够取得更好的效果。 It can even perform favorable against the state-of-the-artmethods when adopting better weak saliency map.
19553 室内定位中半监督学习的指纹库构建方法能够降低人力开销，但忽略了高维接收信号强度(RSS)数据不均匀的非齐分布特点，影响定位精度，针对此问题该文提出一种基于RSS非齐性分布特征的半监督流形对齐指纹库构建方法。 The radio map construction is time consuming and labor intensive, and the conventional semi-supervised based methods usually ignore the influence of the uneven distribution of high-dimensional Received Signal Strength (RSS). In order to solve that problem, a semi-supervised radio map construction approachwhich is based on the nonhomogeneous distribution characteristic of RSS is proposed.
19554 该算法运用局部RSS尺度参数以及共享近邻相似性构造权重矩阵，得到精确反映RSS数据流形结构的权重图，利用该权重图通过求解流形对齐的目标函数最优解，实现运用少量标记数据对大量未标记数据的位置标定。 The approach utilizes theRSS local scale and common neighbors similarities to calculate the weighted matrix. Thus, the weighted graphthat reflects accurately the structure of RSS data manifold is presented. In addition, the weighted graph is usedto find the optimal solution of the objective function to calibrate the locations of plenty of unlabeled data by asmall number of labeled RSS.