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.
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Zhou, B., Zhang, L. (2010). Scene Gist: A Holistic Generative Model of Natural Image. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12304-7_37
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DOI: https://doi.org/10.1007/978-3-642-12304-7_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12303-0
Online ISBN: 978-3-642-12304-7
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