IEEE Trans Pattern Anal Mach Intell - CENTRIST: A Visual Descriptor for Scene Categorization.

Tópicos

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Resumo

CENsus TRansform hISTogram (CENTRIST), 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 satisfies 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 data sets, compared with other descriptors such as SIFT and Gist. Besides, it is easy to implement and evaluates extremely fast.

Resumo Limpo

census transform histogram centrist new visual descriptor recogn topolog place scene categori introduc paper show place scene recognit especi indoor environ requir visual descriptor possess properti differ vision domain eg object recognit centrist satisfi properti suit place scene recognit task holist represent strong generaliz categori recognit centrist main encod structur properti within imag suppress detail textur inform experi demonstr centrist outperform current state art sever place scene recognit data set compar descriptor sift gist besid easi implement evalu extrem fast

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