2009
NIPS
NeurIPS 2009
Segmenting Scenes by Matching Image Composites
Abstract
In this paper, we investigate how similar images sharing the same global description can help with unsupervised scene segmentation in an image. In contrast to recent work in semantic alignment of scenes, we allow an input image to be explained by partial matches of similar scenes. This allows for a better explanation of the input scenes. We perform MRF-based segmentation that optimizes over matches, while respecting boundary information. The recovered segments are then used to re-query a large database of images to retrieve better matches for the target region. We show improved performance in detecting occluding boundaries over previous methods on data gathered from the LabelMe database.
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Trend Setter
— Image Segmentation
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Keyword Pioneer
— semantic alignment
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics
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Interdisciplinary Bridge
— Computer Vision and Machine Learning
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Hot Topic Early Bird
— semantic alignment