IEEE Trans Image Process - High-order local spatial context modeling by spatialized random forest.

Tópicos

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Resumo

In this paper, we propose a novel method for spatial context modeling toward boosting visual discriminating power. We are particularly interested in how to model high-order local spatial contexts instead of the intensively studied second-order spatial contexts, i.e., co-occurrence relations. Motivated by the recent success of random forest in learning discriminative visual codebook, we present a spatialized random forest (SRF) approach, which can encode an unlimited length of high-order local spatial contexts. By spatially random neighbor selection and random histogram-bin partition during the tree construction, the SRF can explore much more complicated and informative local spatial patterns in a randomized manner. Owing to the discriminative capability test for the random partition in each tree node's split process, a set of informative high-order local spatial patterns are derived, and new images are then encoded by counting the occurrences of such discriminative local spatial patterns. Extensive comparison experiments on face recognition and object/scene classification clearly demonstrate the superiority of the proposed spatial context modeling method over other state-of-the-art approaches for this purpose.

Resumo Limpo

paper propos novel method spatial context model toward boost visual discrimin power particular interest model highord local spatial context instead intens studi secondord spatial context ie cooccurr relat motiv recent success random forest learn discrimin visual codebook present spatial random forest srf approach can encod unlimit length highord local spatial context spatial random neighbor select random histogrambin partit tree construct srf can explor much complic inform local spatial pattern random manner owe discrimin capabl test random partit tree node split process set inform highord local spatial pattern deriv new imag encod count occurr discrimin local spatial pattern extens comparison experi face recognit objectscen classif clear demonstr superior propos spatial context model method stateoftheart approach purpos

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