Scene gist: a holistic generative model of natural image

Authors:
Bolei Zhou;Liqing Zhang
Affiliations:
MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems, and Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China;MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems, and Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Year:
2009

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Abstract

This paper proposes a novel generative model for natural image representation and scene classification. Given a natural image, it is decomposed with learned holistic basis called scene gist components. This gist representation is a global and adaptive image descriptor, generatively including most essential information related to visual perception. Meanwhile prior knowledge for scene category is integrated in the generative model to interpret the newly input image. To validate the efficiency of the scene gist representation, a simple nonparametric scene classification algorithm is developed based on minimizing the scene reconstruction error. Finally comparison with other scene classification algorithm is given to show the higher performance of the proposed model.