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Several models have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). The AGR approach significantly accelerates graph-based learning by exploring a set of anchors. However, when a dataset becomes much larger, AGR still faces a big graph which brings dramatically increasing computational costs. To(More)
Many graph-based semi-supervised learning methods for large datasets have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). This model builds a regularization framework by exploring the underlying structure of the whole dataset with both datapoints and anchors. Nevertheless, AGR still has limitations(More)
In recent years, enhancing image details without introducing artifacts has been attracting much attention in image processing community. Various image filtering methods have been proposed to achieve this goal. However, existing methods usually treat all pixels equally during the filtering process without considering the relationship between filtering(More)
Reconstruction of topologically correct and accurate cortical surfaces from infant MR images is of great importance in neuroimaging mapping of early brain development. However, due to rapid growth and ongoing myelination, infant MR images exhibit extremely low tissue contrast and dynamic appearance patterns, thus leading to much more topological errors(More)
Shapes of anatomical structures extracted from medical imaging usually contain diagnostic and therapeutic cues in clinical applications. In this paper, we propose a framework on analyzing disc shapes based on a geodesic metric in an anatomical shape space. All disc shapes, containing both normal and abnormal ones, are formulated as elements in this space.(More)
Online social video websites such as YouTube allow users to manually annotate their video documents with textual labels. These labels can be used as indexing keywords to facilitate search and organization of video data. However, manual video annotation is usually a labor-intensive and time-consuming process. In this work, we propose a novel social video(More)
The scale information in images is important for guiding image-filtering configuration. The authors propose a scale-aware spatially guided mapping (SaSGM) model, which formulates and combines multiple spatial influences of simple edge responses under different levels of detail. The SaSGM model is thus more sensitive to image patterns at a large scale. The(More)