Xiaonan Song

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Feature selection is an important step for large-scale image data analysis, which has been proved to be difficult due to large size in both dimensions and samples. Feature selection firstly eliminates redundant and irrelevant features and then chooses a subset of features that performs as efficient as the complete set. Generally, supervised feature(More)
This paper presents a novel tensor-based feature learning approach for whole-brain fMRI classification. Whole-brain fMRI data have high exploratory power, but they are challenging to deal with due to large numbers of voxels. A critical step for fMRI classification is dimension-ality reduction, via feature selection or feature extraction. Most current(More)
In this paper, we propose an adaptive higher-order CRF (HCRF) labeling approach towards automatic video object segmentation with better performances, higher effectiveness and efficiency. In comparison with the existing state of the arts, our approach achieves further improvements in terms of segmentation results. Our contribution can be highlighted as: (i)(More)
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