Yuzhuo Ren

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In this work, we propose an Expert Decision Fusion (EDF) system to tackle the large-scale indoor/outdoor image classification problem using two key ideas, namely, data grouping and decision stacking. By data grouping, we partition the entire data space into multiple dis-joint sub-spaces so that a more accurate prediction model can be trained in each(More)
The task of estimating the spatial layout of cluttered indoor scenes from a single RGB image is addressed in this work. Existing solutions to this problems largely rely on hand-craft features and vanishing lines, and they often fail in highly cluttered indoor rooms. The proposed coarse-to-fine indoor layout estimation (CFILE) method consists of two stages:(More)
—Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR training. This(More)
—An approach that extracts global attributes from outdoor images to facilitate geometric layout labeling is investigated in this work. The proposed Global-attributes Assisted Labeling (GAL) system exploits both local features and global attributes. First, by following a classical method, we use local features to provide initial labels for all super-pixels.(More)
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