CENTRIST: A Visual Descriptor for Scene Categorization
Rehg, James M.
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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.