CENTRIST: A Visual Descriptor for Scene Categorization
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Wu, Jianxin
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Abstract
CENTRIST (CENsus TRansform hISTogram), a new visual descriptor for recognizing topological
places or scene categories, is introduced in this paper. We show that place and scene recognition,
especially for indoor environments, require its visual descriptor to possess properties that are different
from other vision domains (e.g. object recognition). CENTRIST satisfy these properties and suits the
place and scene recognition task. It is a holistic representation and has strong generalizability for category
recognition. CENTRIST mainly encodes the structural properties within an image and suppresses detailed
textural information. Our experiments demonstrate that CENTRIST outperforms the current state-of-the art
in several place and scene recognition datasets, compared with other descriptors such as SIFT and
Gist. Besides, it is easy to implement. It has nearly no parameter to tune, and evaluates extremely fast.
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2009-07-23
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Technical Report