Xinmiao Ding

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In image understanding, the automatic recognition of emotion in an image is becoming important from an applicative viewpoint. Considering the fact that the emotion evoked by an image is not only from its global appearance but also interplays among local regions, we propose a novel context-aware classification model based on bilayer sparse representation(More)
Comparing with the research of pornographic content filtering on Web, Web horror content filtering, especially horror video scene recognition is still on the stage of exploration. Most existing methods identify horror scene only from independent frames, ignoring the context cues among frames in a video scene. In this paper, we propose a Multi-view(More)
The compound Sr(10)Bi(6)O(24-y) doped with Ni was prepared by solid-state reaction method. The obtained powders were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-vis diffuse reflectance spectra and X-ray photoemission spectra (XPS). The Ni-doped Sr(10)Bi(6)O(24-y) samples assume a cubic perovskite structure with space(More)
In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes(More)
Along with the ever-growing Web, horror video sharing through the Internet has affected our children’s psychological health. Most of current horror video filtering researches pay more attention to the extraction of global features or selection of an optimal classifier, while neglecting the underlying contexts in a scene. In this paper, a novel(More)