Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artif...Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.展开更多
It is often necessary to recognize human mouth-states for detecting the number of audio sources and improving the speech recognition capability of an intelligent robot auditory system. A human mouth-state recognition ...It is often necessary to recognize human mouth-states for detecting the number of audio sources and improving the speech recognition capability of an intelligent robot auditory system. A human mouth-state recognition method based on image warping and sparse representation( SR) combined with homotopy is proposed.Using properly warped training mouth-state images as atoms of the overcomplete dictionary overcomes the impact of the diversity of the mouths' scales,shapes and positions so that further improvement of the robustness can be achieved and the requirement for a large number of training samples can be relieved. The homotopy method is employed to compute the expansion coefficients effectively,i. e.,for sparse coding. The orthogonal matching pursuit( OMP) is also tested and compared with the homototy method. Experimental results and comparisons with the state-of-the-art methods have proved the effectiveness of the proposed approach.展开更多
稀疏保持投影(SPP)是一种基于l1图的新型降维算法,它利用样本间的稀疏重构关系建图,但是SPP为非监督算法,分类效果受到限制。针对此问题,本文提出了一种新的稀疏流形学习算法-稀疏鉴别嵌入(SDE)。该算法在利用样本的稀疏重构关系建图时...稀疏保持投影(SPP)是一种基于l1图的新型降维算法,它利用样本间的稀疏重构关系建图,但是SPP为非监督算法,分类效果受到限制。针对此问题,本文提出了一种新的稀疏流形学习算法-稀疏鉴别嵌入(SDE)。该算法在利用样本的稀疏重构关系建图时引入了样本的类别信息,并通过优化目标函数来得到投影矩阵,使得不同类的数据点在低维嵌入空间中尽可能地分散开。SDE通过结合数据稀疏性及类间流形结构的优点,不仅保留样本间的稀疏重构关系,而且通过引入训练样本的类别信息实现稀疏鉴别特征提取,更有利于分类。在Urban和Washington DC Mall数据集上的实验结果表明:SDE算法比其他算法的分类性能有明显的提升,在每类随机选取16个训练样本的情况下,SDE算法的分类精度分别达到了73.47%和98.35%。展开更多
文摘Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.
基金National Natural Science Foundation of China(No.61210306074)Natural Science Foundation of Jiangxi Province,China(No.2012BAB201025)the Scientific Program of Jiangxi Provincial Education Department,China(Nos.GJJ14583,GJJ13008)
文摘It is often necessary to recognize human mouth-states for detecting the number of audio sources and improving the speech recognition capability of an intelligent robot auditory system. A human mouth-state recognition method based on image warping and sparse representation( SR) combined with homotopy is proposed.Using properly warped training mouth-state images as atoms of the overcomplete dictionary overcomes the impact of the diversity of the mouths' scales,shapes and positions so that further improvement of the robustness can be achieved and the requirement for a large number of training samples can be relieved. The homotopy method is employed to compute the expansion coefficients effectively,i. e.,for sparse coding. The orthogonal matching pursuit( OMP) is also tested and compared with the homototy method. Experimental results and comparisons with the state-of-the-art methods have proved the effectiveness of the proposed approach.
文摘稀疏保持投影(SPP)是一种基于l1图的新型降维算法,它利用样本间的稀疏重构关系建图,但是SPP为非监督算法,分类效果受到限制。针对此问题,本文提出了一种新的稀疏流形学习算法-稀疏鉴别嵌入(SDE)。该算法在利用样本的稀疏重构关系建图时引入了样本的类别信息,并通过优化目标函数来得到投影矩阵,使得不同类的数据点在低维嵌入空间中尽可能地分散开。SDE通过结合数据稀疏性及类间流形结构的优点,不仅保留样本间的稀疏重构关系,而且通过引入训练样本的类别信息实现稀疏鉴别特征提取,更有利于分类。在Urban和Washington DC Mall数据集上的实验结果表明:SDE算法比其他算法的分类性能有明显的提升,在每类随机选取16个训练样本的情况下,SDE算法的分类精度分别达到了73.47%和98.35%。
